CN114266594A - Big data analysis method based on southeast Asia cross-border e-commerce platform - Google Patents
Big data analysis method based on southeast Asia cross-border e-commerce platform Download PDFInfo
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Abstract
The invention discloses a big data analysis method based on a southeast Asia cross-border e-commerce platform, which comprises the following steps: acquiring original data information from a cross-border e-commerce platform; classifying the original data information into a plurality of different data sets according to the classification after processing; analyzing the data set, and carrying out keyword index marking on the data of the data set class; and establishing a search model, and substituting the keywords of different data sets to obtain the required data set. The big data analysis method based on the southeast Asia cross-border e-commerce platform is convenient to use, effectively achieves the big data analysis process of the e-commerce platform, is high in analysis efficiency and good in analysis effect, can efficiently obtain various required key data, and therefore is high in practicability.
Description
Technical Field
The invention relates to a big data analysis method based on a southeast Asia cross-border e-commerce platform, and relates to the technical field of cross-border e-commerce.
Background
The cross-border electronic commerce refers to a transaction subject belonging to different customs, achieves transaction and electronic payment settlement through an electronic commerce platform, and delivers commodities through cross-border electronic commerce logistics and remote storage, thereby completing an international business activity of transaction. The cross-border electronic commerce is developed based on the network, and the network space is a new space relative to the physical space and is a virtual but objective world consisting of a website and a password. The unique value standard and behavior mode of the network space profoundly influence cross-border electronic commerce, so that the cross-border electronic commerce has the characteristics different from the traditional transaction mode. However, many current cross-border e-commerce platforms do not have a high-efficiency big data analysis method, and thus cannot rapidly make a sales pattern to meet the purchase demands of different customers in different time periods.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the big data analysis method based on the southeast Asia cross-border E-commerce platform is convenient to operate and capable of effectively improving analysis efficiency.
In order to solve the technical problems, the invention is realized by the following technical scheme:
a big data analysis method based on a southeast Asia cross-border e-commerce platform comprises the following steps:
s1, acquiring original data information from the cross-border e-commerce platform;
s2, classifying the original data information into a plurality of different data sets according to the processed original data information;
s3, analyzing the data set, and performing keyword index marking on the data of the data set class;
and S4, establishing a search model, and substituting the keywords of different data sets to obtain the required data set.
Preferably, the raw data information collected in step S1 includes product information, purchase time information, price information, customer region distribution information, customer gender information, and the industry information.
Preferably, in the step S2, in the data processing, the data set classification method includes, first, classifying the data information according to the collection type to obtain a data information set a; then, sorting the data in the data information classification set to obtain a data information set B; and deleting repeated data in the data information set B to obtain a required data set.
Preferably, in step S2, the classified data sets include a product data set, a purchase time data set, a price data set, a customer region distribution data set, a customer gender data set, the industry data set, and the like.
Preferably, in step S2, the data set includes a plurality of subsets, each subset including a data set corresponding to a minimum subdivision unit.
Preferably, in the step S3, in the keyword index labeling of the data set class, a predetermined lexicon is first designated for each keyword of the data set, and when the keyword needs to be used, the corresponding lexicon is called.
Preferably, the data search model comprises an input unit, a keyword processing and calling unit, a data processing unit and a data output unit, wherein a user inputs keywords through the input unit, the keyword processing and calling unit calls a word library of the keywords and analyzes the words and outputs signals to the data processing unit, and the data processing unit calls corresponding data set information according to the keyword signals and outputs the data set information through the data output unit.
Compared with the prior art, the invention has the advantages that: the big data analysis method based on the southeast Asia cross-border e-commerce platform is convenient to use, effectively achieves the big data analysis process of the e-commerce platform, is high in analysis efficiency and good in analysis effect, can efficiently obtain various required key data, and therefore is high in practicability.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention:
a big data analysis method based on a southeast Asia cross-border e-commerce platform comprises the following steps of firstly, collecting original data information from the cross-border e-commerce platform; in this embodiment, in order to facilitate analysis of the required data, the collected original data information includes product information, purchase time information, price information, customer regional distribution information, customer gender information, industry information, and the like, where the product information includes information such as names, brands, models, and the like of each commodity, the purchase time information includes purchase time information of each product in all periods, the customer regional distribution information includes information of regions to which customers belong, such as country and specific administrative region, the industry information includes information such as industry categories to which purchased commodities belong, and the price information includes unit price, average price, domestic average price, foreign price information, and the like of the commodity
Then classifying the original data information into a plurality of different data sets according to the processed original data information; in this embodiment, to facilitate classification, in the data processing, the data set classification method includes, first, classifying the data information according to an acquisition type to obtain a data information set a; then, sorting the data in the data information classification set to obtain a data information set B; and deleting repeated data in the data information set B to obtain a required data set. The obtained data set is the ordered data set without data repetition, so that the data set is convenient to view and index. In addition, the categorized data sets include a product data set, a purchase time data set, a price data set, a customer geographic distribution data set, a customer gender data set, the industry data set, and the like. The corresponding original data are respectively product information, purchasing time information, price information, customer region distribution information, customer gender information and the industry information; in addition, to facilitate sorting and index viewing, the data set includes a plurality of subsets, each subset containing a data set corresponding to a minimum subdivision unit. The minimum subdivision unit here is the minimum subdivision unit in the product category, for example, in the product data set, each subset corresponds to data information corresponding to each product, including purchase time information, price information, customer regional distribution information, customer gender information, the industry information, and the like of the corresponding product, in each subset, corresponding data is sorted and counted, for example, in the product subset, in the corresponding purchase time data, time points or time periods corresponding to the corresponding product are counted and sorted, and the unit number is counted; in the price information data, counting and sequencing each price point and price range of the corresponding product and counting the unit quantity; in the customer region distribution information, the region distribution information of the customers purchased by the corresponding products is counted and ranked, and the unit number is counted, in the customer gender information, the gender information of the customers is counted, and the unit number is counted, and in the industry information data, the industry information to which the corresponding products belong is counted. And in other data sets, the above mode is also adopted, for example, the subset of the purchase time data set is each time period and time node, the subset of the price data set is each price range and price point, the subset of the customer region distribution data set is country name and subdivided administrative unit, the subset of the customer gender data set is male and female subsets, the subset of the industry data set is name set of different industries, and the data corresponding to different subsets is also subjected to ranking statistics by adopting the above mode to obtain data of other categories except for the category required by each subset.
Then, analyzing the data set, and carrying out keyword index marking on the data of the data set class; in the process of carrying out key word index marking on data of a data set class, a specified word stock is firstly appointed to a key word of each data set, and when the key word needs to be used, the corresponding word stock is called. In keyword tagging of a data set, the tagging is typically done with a category of the data set or a category of a subset within the data set. Thus, when selected at the corresponding keyword, data of the corresponding data set or subset within the data set can be extracted.
And finally, establishing a search model, and substituting the keywords of different data sets to obtain the required data set. In order to conveniently obtain required data, the data search model comprises an input unit, a keyword processing and calling unit, a data processing unit and a data output unit, a user inputs keywords through the input unit, the keyword processing and calling unit calls a word bank of the keywords and analyzes the keywords and outputs signals to the data processing unit, and the data processing unit calls corresponding data set information according to the keyword signals and outputs the data through the data output unit. Therefore, when the user inputs the keyword, the word stock is transferred to be compared with the input keyword, the data of the data set corresponding to the marked keyword is called after the corresponding marked keyword is obtained, and the required data is obtained through the data output unit, for example, when the user inputs the product name, the purchase quantity of all the purchase time periods or time points of the corresponding product, the purchase quantity of all the price sections or price points, the proportion quantity of men and women purchased by the client, the regional distribution condition of the client, such as the most purchasing countries, the least purchasing countries and the like, and the industry information and the like of the product are output.
It is to be emphasized that: the above embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention in any way, and all simple modifications, equivalent changes and modifications made to the above embodiments according to the technical spirit of the present invention are within the scope of the technical solution of the present invention.
Claims (7)
1. A big data analysis method based on a southeast Asia cross-border e-commerce platform comprises the following steps:
s1, acquiring original data information from the cross-border e-commerce platform;
s2, classifying the original data information into a plurality of different data sets according to the processed original data information;
s3, analyzing the data set, and performing keyword index marking on the data of the data set class;
and S4, establishing a search model, and substituting the keywords of different data sets to obtain the required data set.
2. The big data analysis method based on the southeast Asia cross-border E-commerce platform according to claim 1, wherein: in step S1, the collected original data information includes product information, purchase time information, price information, customer region distribution information, customer gender information, the industry information, and the like.
3. The big data analysis method based on the southeast Asia cross-border E-commerce platform according to claim 2, wherein: in the step S2, in the data processing, the data set classification method includes, firstly, classifying the data information according to the collection type to obtain a data information set a; then, sorting the data in the data information classification set to obtain a data information set B; and deleting repeated data in the data information set B to obtain a required data set.
4. The big data analysis method based on the southeast Asia cross-border E-commerce platform according to claim 3, wherein: in step S2, the classified data sets include a product data set, a purchase time data set, a price data set, a customer region distribution data set, a customer gender data set, the industry data set, and the like.
5. The big data analysis method based on the southeast Asia cross-border E-commerce platform according to claim 4, wherein: in step S2, the data set includes a plurality of subsets, each subset including a data set corresponding to a minimum subdivision unit.
6. The big data analysis method based on the southeast Asia cross-border E-commerce platform according to claim 1, wherein: in the step S3, in the keyword index labeling of the data set class, a predetermined lexicon is first specified for each keyword of the data set, and when the keyword needs to be used, the corresponding lexicon is called.
7. The big data analysis method based on the southeast Asia cross-border E-commerce platform according to claim 1, wherein: the data search model comprises an input unit, a keyword processing and calling unit, a data processing unit and a data output unit, wherein a user inputs keywords through the input unit, the keyword processing and calling unit calls and analyzes a word library of the keywords and outputs signals to the data processing unit, and the data processing unit calls corresponding data set information according to the keyword signals and then outputs the data set information through the data output unit.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116596570A (en) * | 2023-05-11 | 2023-08-15 | 广东德澳智慧医疗科技有限公司 | Information comparison system of same product in different E-commerce platforms based on big data analysis algorithm |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116596570A (en) * | 2023-05-11 | 2023-08-15 | 广东德澳智慧医疗科技有限公司 | Information comparison system of same product in different E-commerce platforms based on big data analysis algorithm |
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