CN110399455A - A kind of deep learning data digging method based on CNN and LSTM - Google Patents
A kind of deep learning data digging method based on CNN and LSTM Download PDFInfo
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- CN110399455A CN110399455A CN201910488085.5A CN201910488085A CN110399455A CN 110399455 A CN110399455 A CN 110399455A CN 201910488085 A CN201910488085 A CN 201910488085A CN 110399455 A CN110399455 A CN 110399455A
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Abstract
A kind of deep learning data digging method based on CNN and LSTM, comprising the following steps: the feature vector for successively collecting each target object obtains raw data packets；To data prediction, character information library is established；Construct CNN-LSTM algorithm model；CNN-LSTM algorithm model is trained, and is tested；Data mining is carried out using CNN-LSTM algorithm model；Data feedback is obtained, is learnt.In the present invention, data mining is completed in study by building LSTM-CNN algorithm model, the efficiency of acquisition of information is substantially increased, solves the problems, such as that conventional information transfers difficulty, learner is helped to improve learning effect；Wherein CNN quickly identifies the corresponding character information of keyword, and LSTM quickly extends to the relevant information of keyword, supplemented using the form of replicated blocks chain, so as to transfer speed fast for information, information transfers accuracy height.
Description
Technical field
The present invention relates to learning areas more particularly to a kind of deep learning data digging methods based on CNN and LSTM.
Background technique
Currently, electronization is realized in the work of each department and each area substantially.Due to realizing that the time of electronization is not grown, believe
Breath amount is excessively huge, so that the accuracy of information excavating is low, speed is slow, has delayed constituent parts and personal study.
To solve the above problems, proposing a kind of deep learning data digging method based on CNN and LSTM in the application.
Summary of the invention
(1) goal of the invention
To solve technical problem present in background technique, the present invention proposes a kind of deep learning based on CNN and LSTM
Data digging method, the present invention complete data mining in study by building LSTM-CNN algorithm model, substantially increase letter
The efficiency obtained is ceased, solves the problems, such as that conventional information transfers difficulty, learner is helped to improve learning effect；Wherein CNN is to key
The corresponding character information of word is quickly identified that LSTM quickly prolongs the relevant information of keyword using the form of replicated blocks chain
Exhibition, supplement, so as to transfer speed fast for information, information transfers accuracy height.
(2) technical solution
To solve the above problems, the present invention provides a kind of deep learning data digging method based on CNN and LSTM, packet
Include following steps:
S1, the feature vector for successively collecting each target object, obtain raw data packets；
S2, to data prediction, establish character information library；
S3, building CNN-LSTM algorithm model；
S4, CNN-LSTM algorithm model is trained, and tested；
S5, data mining is carried out using CNN-LSTM algorithm model；
S6, data feedback is obtained, is learnt.
Preferably, in S1, feature vector is multiple attribute datas of target object, including party member's name, identity card letter
Breath, party membership, time of joining the party, the time limit of joining the party, post, work location.
Preferably, in S2, the mode of data prediction are as follows: with name, identity card and party member's information for main keyword,
Classify to the data in raw data packets, and be preset format by Data Format Transform, obtains character information library.
Preferably, in S3, CNN nerve network system and LSTM nerve network system are series connection modeling.
Preferably, in S4, the CNN model of construction includes input layer, convolutional layer, full articulamentum and output layer；It is wherein defeated
The length for entering the one-dimensional data sequence of layer input is 2k+1；Convolution kernel is one-dimentional structure, size 2k+1 in convolutional layer；Quan Lian
It connects layer and builds decline in CNN model；The downstream of full articulamentum is arranged in output layer.
Preferably, in S4, LSTM includes length memory layer and at least one full articulamentum.
Preferably, when carrying out data mining in S5, keyword is inputted first, CNN is to the corresponding character information of keyword
It is quickly identified, LSTM quickly extends to the relevant information of keyword, supplemented using the form of replicated blocks chain.
It preferably, further include correction system；In S6, the data that learner is fed back, further according to the accuracy of data
It is corrected, constantly improve model.
It preferably, further include record system；Record system records the data content of excavation, time, frequency, according to
Statistical conditions preferentially push learner, reduce the time of data mining.
Above-mentioned technical proposal of the invention has following beneficial technical effect:
One, in the present invention, data mining is completed in study by building LSTM-CNN algorithm model, substantially increases letter
The efficiency obtained is ceased, solves the problems, such as that conventional information transfers difficulty, learner is helped to improve learning effect；Wherein CNN is to key
The corresponding character information of word is quickly identified that LSTM quickly prolongs the relevant information of keyword using the form of replicated blocks chain
Exhibition, supplement, so as to transfer speed fast for information, information transfers accuracy height.
Two, in the present invention, the correction system and record system of setting improve the accuracy and convenience of data mining,
Further improving method system, promotes learning effect.
Detailed description of the invention
Fig. 1 is a kind of flow chart of the deep learning data digging method based on CNN and LSTM proposed by the present invention.
Specific embodiment
In order to make the objectives, technical solutions and advantages of the present invention clearer, With reference to embodiment and join
According to attached drawing, the present invention is described in more detail.It should be understood that these descriptions are merely illustrative, and it is not intended to limit this hair
Bright range.In addition, in the following description, descriptions of well-known structures and technologies are omitted, to avoid this is unnecessarily obscured
The concept of invention.
As shown, a kind of deep learning data digging method based on CNN and LSTM proposed by the present invention, including it is following
Step:
S1, the feature vector for successively collecting each target object, obtain raw data packets；
S2, to data prediction, establish character information library；
S3, building CNN-LSTM algorithm model；
S4, CNN-LSTM algorithm model is trained, and tested；
S5, data mining is carried out using CNN-LSTM algorithm model；
S6, data feedback is obtained, is learnt.
In an alternative embodiment, in S1, feature vector is multiple attribute datas of target object, including party member
Name, ID card information, party membership, time of joining the party, the time limit of joining the party, post, work location.
In an alternative embodiment, in S2, the mode of data prediction are as follows: with name, identity card and party member letter
Breath is main keyword, is classified to the data in raw data packets, and is preset format by Data Format Transform, obtains word
Accord with information bank.
In an alternative embodiment, in S3, CNN nerve network system and LSTM nerve network system are that series connection is built
Mould.
In an alternative embodiment, in S4, the CNN model of construction include input layer, convolutional layer, full articulamentum and
Output layer；Wherein the length of the one-dimensional data sequence of input layer input is 2k+1；Convolution kernel is one-dimentional structure in convolutional layer, big
Small is 2k+1；Full articulamentum builds the decline in CNN model；The downstream of full articulamentum is arranged in output layer.
In an alternative embodiment, in S4, LSTM includes length memory layer and at least one full articulamentum.
In an alternative embodiment, when carrying out data mining in S5, keyword is inputted first, CNN is to keyword
Corresponding character information is quickly identified that LSTM quickly prolongs the relevant information of keyword using the form of replicated blocks chain
Exhibition, supplement.
It in an alternative embodiment, further include correction system；In S6, the data that learner is fed back, then root
It is corrected according to the accuracy of data, constantly improve model.
It in an alternative embodiment, further include record system；Record system is to the data content of excavation, time, frequency
Rate is recorded, and is preferentially pushed according to statistical conditions to learner, reduces the time of data mining.
In the present invention, data mining is completed in study by building LSTM-CNN algorithm model, substantially increases information
The efficiency of acquisition solves the problems, such as that conventional information transfers difficulty, learner is helped to improve learning effect；Wherein CNN is to keyword
Corresponding character information is quickly identified that LSTM quickly prolongs the relevant information of keyword using the form of replicated blocks chain
Exhibition, supplement, so as to transfer speed fast for information, information transfers accuracy height；The correction system and record system of setting, improve number
According to the accuracy and convenience of excavation, further improving method system promotes learning effect.
It should be understood that above-mentioned specific embodiment of the invention is used only for exemplary illustration or explains of the invention
Principle, but not to limit the present invention.Therefore, that is done without departing from the spirit and scope of the present invention is any
Modification, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.In addition, appended claims purport of the present invention
Covering the whole variations fallen into attached claim scope and boundary or this range and the equivalent form on boundary and is repairing
Change example.
Claims (9)
1. a kind of deep learning data digging method based on CNN and LSTM, which comprises the following steps:
S1, the feature vector for successively collecting each target object, obtain raw data packets；
S2, to data prediction, establish character information library；
S3, building CNN-LSTM algorithm model；
S4, CNN-LSTM algorithm model is trained, and tested；
S5, data mining is carried out using CNN-LSTM algorithm model；
S6, data feedback is obtained, is learnt.
2. a kind of deep learning data digging method based on CNN and LSTM according to claim 1, which is characterized in that
In S1, feature vector is multiple attribute datas of target object, including party member's name, ID card information, party membership, when joining the party
Between, the time limit of joining the party, post, work location.
3. a kind of deep learning data digging method based on CNN and LSTM according to claim 1, which is characterized in that
In S2, the mode of data prediction are as follows: with name, identity card and party member's information for main keyword, in raw data packets
Data classify, and by Data Format Transform be preset format, obtain character information library.
4. a kind of deep learning data digging method based on CNN and LSTM according to claim 1, which is characterized in that
In S3, CNN nerve network system and LSTM nerve network system are series connection modeling.
5. a kind of deep learning data digging method based on CNN and LSTM according to claim 1, which is characterized in that
In S4, the CNN model of construction includes input layer, convolutional layer, full articulamentum and output layer；A wherein dimension of input layer input
Length according to sequence is 2k+1；Convolution kernel is one-dimentional structure, size 2k+1 in convolutional layer；Full articulamentum is built in CNN mould
The decline of type；The downstream of full articulamentum is arranged in output layer.
6. a kind of deep learning data digging method based on CNN and LSTM according to claim 1, which is characterized in that
In S4, LSTM includes length memory layer and at least one full articulamentum.
7. a kind of deep learning data digging method based on CNN and LSTM according to claim 1, which is characterized in that
When carrying out data mining in S5, input keyword, CNN quickly identify the corresponding character information of keyword first,
LSTM quickly extends to the relevant information of keyword, is supplemented using the form of replicated blocks chain.
8. a kind of deep learning data digging method based on CNN and LSTM according to claim 1, which is characterized in that
It further include correction system；In S6, the data that learner is fed back are corrected further according to the accuracy of data, constantly complete
Kind model.
9. a kind of deep learning data digging method based on CNN and LSTM according to claim 1, which is characterized in that
It further include record system；Record system records the data content of excavation, time, frequency, according to statistical conditions to study
Person is preferentially pushed, and the time of data mining is reduced.
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| CN111915218A (en) * | 2020-08-14 | 2020-11-10 | 中国工商银行股份有限公司 | Financial counterfeiting identification method and system based on LSTM-CNN |
| CN112052853A (en) * | 2020-09-09 | 2020-12-08 | 国家气象信息中心 | Text positioning method of handwritten meteorological archive data based on deep learning |
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