Intelligent analysis method and system for medical literature data based on natural language processing

Abstract

The invention relates to the technical field of data analysis, and discloses an intelligent medical literature data analysis method and system based on natural language processing. The method comprises the following steps: collecting historical medical literature data, processing the collected historical medical literature data, constructing a sample set based on the obtained processed historical medical literature data, and constructing a literature topic analysis model based on the processed historical medical literature data in the sample set. The subject of the processed historical medical literature data is determined, and meanwhile, based on the determined subject of the processed historical medical literature data, the subject of the processed historical medical literature data is analyzed and trained through an intelligent analysis method; after the training is completed, the medical literature data is collected in real time, analysis and matching are carried out through a natural language processing mode based on the obtained trained historical medical literature data theme, and the accuracy of medical literature data analysis is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a method and system for intelligent analysis of medical literature data based on natural language processing. Background Art

[0002] With the continuous increase of medical literature data, the difficulty of manual inspection and testing of medical literature is increasing. The pain points of medical literature management such as low efficiency and proneness to errors have made it urgent to use medical literature intelligent analysis systems to further improve the recognition rate of medical critical values ​​and reduce human errors.

[0003] The existing open patent application CN118916459A, this method obtains the subject words in the document by collecting and traversing the combination, and records a word combination composed of subject words as the first target combination, and marks all the first target combinations in the document. After the marking is completed, the second target combination composed of subject words different from the first target combination is collected and traversed; and in each paragraph of the document, the distribution of all subject words constituting the target combination is statistically analyzed to obtain the distribution vector of the subject words in each paragraph, and represented by a reference vector group to obtain the comparison vector of the target combination, and at the same time obtain several documents including the target combination, compare them with the reference documents, and compare the comparison vector of the reference documents with the comparison vectors of several documents to obtain the difference of the subject words in different documents. However, since the document data is analyzed only by obtaining the subject words and combining them, the intelligence is insufficient and the analysis is inaccurate. Summary of the invention

[0004] 1. Technical issues to be resolved In view of the shortcomings of the prior art, the present invention provides a method and system for intelligent analysis of medical literature data based on natural language processing, which has the advantages of high efficiency, real-time, intelligence, and accuracy, and solves the pain points of low efficiency and easy errors in medical literature management.

[0005] (II) Technical solution In order to solve the above-mentioned technical problems of low efficiency and easy errors in medical literature management, the present invention provides the following technical solutions: The present invention discloses a method for intelligent analysis of medical literature data based on natural language processing, which specifically comprises the following steps: S1. Collecting historical medical literature data, processing the collected historical medical literature data to obtain processed historical medical literature data, and constructing a sample set based on the processed historical medical literature data; S2. Based on the historical medical literature data processed in the sample set, a literature theme analysis model is constructed, and the theme of the processed historical medical literature data is determined based on the constructed literature theme analysis model; S3, analyzing and training the processed historical medical literature data themes through intelligent analysis methods to obtain a trained historical medical literature data theme set; S4. Collect medical literature data in real time, determine the theme of the medical literature data collected in real time based on the constructed literature theme analysis model, and at the same time, analyze and match the theme set of historical medical literature data after training through natural language processing to obtain the analyzed theme of historical medical literature data.

[0006] The present invention collects historical medical literature data, processes the collected historical medical literature data, and constructs a sample set based on the processed historical medical literature data. At the same time, based on the processed historical medical literature data in the sample set, a document theme analysis model is constructed, and the theme of the processed historical medical literature data is determined. At the same time, based on the theme of the processed historical medical literature data, the theme of the processed historical medical literature data is analyzed and trained through an intelligent analysis method. After the training is completed, the medical literature data is collected in real time based on the theme of the trained historical medical literature data, and analyzed and matched through natural language processing, thereby improving the accuracy of the medical literature data analysis.

[0007] Preferably, the collecting of historical medical literature data, processing the collected historical medical literature data to obtain processed historical medical literature data, and constructing a sample set based on the obtained processed historical medical literature data comprises the following steps: S11, collecting historical medical literature data, filtering and processing the collected historical medical literature data to obtain processed historical medical literature data; S12, constructing a sample set based on the processed historical medical literature data; Building a sample set ; in, represents the sample set, represents the first set of processed historical medical literature data in the sample set, The nth group of processed historical medical literature data in the sample set.

[0008] Preferably, filtering the collected historical medical literature data to obtain the processed historical medical literature data comprises the following steps: Create a length of Array, select A hash function is used to calculate the name and time of each collected historical medical literature data, and the calculation results are stored in an array; in, represents the number of hash functions, Indicates the amount of historical medical literature data collected; In the process of calculating the collected historical medical literature data using the hash function, when the calculation results of two sets of historical medical literature data are consistent, the names and times in the two sets of historical medical literature data are compared; When the comparison results are consistent, the current two sets of historical medical literature data are set as duplicate historical medical literature data; When two sets of historical medical literature data are duplicate historical medical literature data, one set of historical medical literature data is deleted to implement filtering processing; After the calculation is completed, the historical medical literature data stored in the array during the calculation process is summarized to obtain the processed historical medical literature data.

[0009] The present invention ensures the reliability of the historical medical literature data by filtering the collected historical medical literature data and constructing a sample set based on the processed historical medical literature data.

[0010] Preferably, the process of constructing a document theme analysis model based on the processed historical medical document data in the sample set, and determining the theme of the processed historical medical document data based on the constructed document theme analysis model comprises the following steps: S21. Randomly select a set of processed historical medical literature data from the sample set, extract all the word pairs, and construct a word pair set ; S22. For vocabulary pair sets For each word pair in , calculate the probability that the word pair becomes the subject of the processed historical medical literature data; ; in, Represents a set of vocabulary pairs Chinese word pair Become the first The probability of the topic of historical medical literature data after group processing, Indicated in Word pairs in historical medical literature data after group processing Number of occurrences; S23. Based on the calculated probability of each word pair becoming a topic, a document topic analysis model is constructed to determine the topic of the processed historical medical document data.

[0011] Preferably, the process of constructing a document topic analysis model based on the calculated probability of each word pair becoming a topic and determining the topic of the processed historical medical document data comprises the following steps: The formula for calculating word pair weights is as follows: ; in, Representing word pairs The weight of represents the probability parameter, Represents positional parameters, Representing word pairs Location in historical medical literature data, including title, subheadings, text, and abstract; Combine the word pair probability calculation formula and the word pair weight calculation formula, and set the combined word pair probability calculation formula and word pair weight calculation formula as the literature topic analysis model; The weight threshold of the word pairs in the literature topic analysis model is set, the word pairs with weights greater than or equal to the weight threshold are summarized, and the theme of the processed historical medical literature data is determined based on the summarized word pairs.

[0012] The present invention selects a group of processed historical medical literature data, extracts all word pairs, calculates the probability of a word pair becoming a topic, constructs a document topic analysis model based on the calculated probability, determines the topic of the processed historical medical literature data, and improves the accuracy of the medical literature data.

[0013] Preferably, the process of analyzing and training the processed historical medical literature data themes by intelligent analysis to obtain a trained historical medical literature data theme set comprises the following steps: S31, selecting some of the processed historical medical literature data topics from the determined processed historical medical literature data topics to construct a training set; S32, inputting the themes of the historical medical literature data processed in the training set into the BP neural network, and determining the BP neural network model through iterative training; S33. Based on the determined BP neural network model, all processed historical medical literature data topics are trained, and a set of trained historical medical literature data topics is output.

[0014] Preferably, the process of inputting the historical medical literature data themes processed in the training set into the BP neural network and determining the BP neural network model by iterative training comprises the following steps: The themes of historical medical literature data processed in the training set are input into the BP neural network. The structure of the BP neural network includes: input layer, hidden layer and output layer; Set the input set of the input layer to ,in Indicates the input The total number of historical medical literature data themes processed in the input training set is ; The output set of the output layer is ,in Indicates the output The total number of historical medical literature data topics after training is output as ; Assume that the hidden layer contains q neurons, v is the weight from the input layer to the hidden layer, and w is the weight from the hidden layer to the output layer; Training is performed through the forward propagation process of the BP neural network; During the forward propagation process, the calculation formula for transferring data from the input layer to the hidden layer is: ; in, represents the input of the hth hidden layer neuron, represents the weight of the i-th input of the input layer to the h-th hidden layer neuron, and m represents the number of input data of the input layer; Represents the bias value of the hth hidden layer neuron; The calculation formula for data transmission from the hidden layer to the output layer is: ; in, represents the input of the jth output layer neuron, represents the weight from the hth hidden layer neuron to the jth output layer neuron, and q represents the number of hidden layer neurons; represents the output of the hth hidden layer neuron; Represents the bias value of the jth output layer neuron; After receiving the parameter data transmitted by the hidden layer, the output layer directly outputs the parameter data received from the hidden layer; Right now ; Calculate the error between the output layer and the expected value; Set the error threshold between the output layer and the expected value. When the error between the output layer and the expected value is greater than or equal to the set error threshold, iterative calculation is performed by adjusting the weights from the hidden layer to the output layer and the weights from the input layer to the hidden layer in turn until a BP neural network model is obtained. The iterative calculation formula is as follows: ; in, represents the weight adjustment value, represents the learning rate, Indicates the error, Represents the output of the output layer.

[0015] The present invention improves the intelligence of medical literature data analysis by selecting themes of some historical medical literature data from the themes of processed historical medical literature data to construct a training set, and training the themes of all processed historical medical literature data through the forward propagation process of the BP neural network.

[0016] Preferably, the real-time collection of medical literature data, determining the theme of the real-time collected medical literature data based on the constructed literature theme analysis model, and simultaneously analyzing and matching the obtained trained historical medical literature data theme set through natural language processing to obtain the analyzed historical medical literature data theme include the following steps: Collect medical literature data in real time, determine the theme of the medical literature data collected in real time based on the constructed literature theme analysis model, and calculate the similarity between the theme of the medical literature data collected in real time and the theme set of historical medical literature data after training through the Jaccard coefficient; The calculation formula is: ; Among them, KT is the real-time collection of medical literature data themes, KN ​​is the training of historical medical literature data themes, Represents the similarity between set KT and set KN; The Jaccard similarity threshold is set, and analysis and matching are performed through similarity matching. When the similarity between the real-time collected medical literature data theme and the trained historical medical literature data theme is greater than or equal to the threshold, it is determined that the real-time collected medical literature data theme is related to the trained historical medical literature data theme. If the similarity is lower than the threshold, it is determined that the real-time collected medical literature data theme is not related to the trained historical medical literature data theme.

[0017] The present invention determines the subject of real-time collected medical literature data based on a constructed document subject analysis model, and calculates the similarity between the subject of real-time collected medical literature data and the subject of trained historical medical literature data through the Jaccard coefficient, thereby improving the real-time performance of medical literature data analysis.

[0018] The present invention also discloses a medical literature data intelligent analysis system based on natural language processing, which is used to implement a medical literature data intelligent analysis method based on natural language processing. The system includes: a data acquisition module, a data processing module, an analysis and training module, and a natural language processing module; The data acquisition module is used to receive medical literature data in real time; The data processing module is used to process the received medical literature data and transmit the processed medical literature data to the analysis and training module; The analysis and training module is used to analyze the received processed medical literature data; The natural language processing module is used to determine real-time medical literature data by calculating relevance.

[0019] (III) Beneficial effects Compared with the prior art, the present invention provides a method and system for intelligent analysis of medical literature data based on natural language processing, which has the following beneficial effects: 1. The invention collects historical medical literature data, processes the collected historical medical literature data, and constructs a sample set based on the processed historical medical literature data. At the same time, based on the processed historical medical literature data in the sample set, a document theme analysis model is constructed, and the theme of the processed historical medical literature data is determined. At the same time, based on the theme of the processed historical medical literature data, the theme of the processed historical medical literature data is analyzed and trained through an intelligent analysis method. After the training is completed, the medical literature data is collected in real time based on the theme of the trained historical medical literature data, and analyzed and matched through natural language processing, thereby improving the accuracy of the medical literature data analysis.

[0020] 2. The invention ensures the reliability of historical medical literature data by filtering the collected historical medical literature data and constructing a sample set based on the processed historical medical literature data.

[0021] 3. The invention selects a group of processed historical medical literature data, extracts all word pairs, and calculates the probability of a word pair becoming a topic. Based on the calculated probability, a document topic analysis model is constructed to determine the topic of the processed historical medical literature data, thereby improving the efficiency of medical literature data.

[0022] 4. The invention improves the intelligence of medical literature data analysis by selecting themes of some historical medical literature data from the themes of processed historical medical literature data to construct a training set, and training the themes of all processed historical medical literature data through the forward propagation process of the BP neural network.

[0023] 5. The present invention determines the subject of medical literature data collected in real time based on a constructed literature subject analysis model, and calculates the similarity between the subject of medical literature data collected in real time and the subject of historical medical literature data after training through the Jaccard coefficient, thereby improving the real-time performance of medical literature data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the structure of the medical literature data intelligent analysis process of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Example 1 See also Figure 1 This embodiment discloses a method for intelligent analysis of medical literature data based on natural language processing, which specifically includes the following steps: S1. Collecting historical medical literature data, processing the collected historical medical literature data to obtain processed historical medical literature data, and constructing a sample set based on the processed historical medical literature data; S2. Based on the historical medical literature data processed in the sample set, a literature theme analysis model is constructed, and the theme of the processed historical medical literature data is determined based on the constructed literature theme analysis model; S3, analyzing and training the processed historical medical literature data themes through intelligent analysis methods to obtain a trained historical medical literature data theme set; S4, collecting medical literature data in real time, determining the theme of the medical literature data collected in real time based on the constructed literature theme analysis model, and at the same time, analyzing and matching the theme set of historical medical literature data after training through natural language processing to obtain the analyzed theme of historical medical literature data; For further information, see Figure 1 , collecting historical medical literature data, processing the collected historical medical literature data to obtain processed historical medical literature data, and constructing a sample set based on the obtained processed historical medical literature data includes the following steps: S11, collecting historical medical literature data, filtering and processing the collected historical medical literature data to obtain processed historical medical literature data; Create a length of Array, select A hash function is used to calculate the name and time of each collected historical medical literature data, and the calculation results are stored in an array; in, represents the number of hash functions, Indicates the amount of historical medical literature data collected; In the process of calculating the collected historical medical literature data using the hash function, when the calculation results of two sets of historical medical literature data are consistent, the names and times in the two sets of historical medical literature data are compared; When the comparison results are consistent, the current two sets of historical medical literature data are set as duplicate historical medical literature data; When two sets of historical medical literature data are duplicate historical medical literature data, one set of historical medical literature data is deleted to implement filtering processing; After the calculation is completed, the historical medical literature data stored in the array during the calculation process is summarized to obtain the processed historical medical literature data; S12, constructing a sample set based on the processed historical medical literature data; Building a sample set ; in, represents the sample set, represents the first set of processed historical medical literature data in the sample set, The nth group of processed historical medical literature data in the sample set; Furthermore, the historical medical literature data after centralized sample processing includes diverse data such as literature content, literature citation information, and time information; For further information, see Figure 1 Based on the processed historical medical literature data in the sample set, a literature subject analysis model is constructed, and the subject of the processed historical medical literature data is determined based on the constructed literature subject analysis model, including the following steps: S21. Randomly select a set of processed historical medical literature data from the sample set, extract all the word pairs, and construct a word pair set ; S22. For vocabulary pair sets For each word pair in , calculate the probability that the word pair becomes the subject of the processed historical medical literature data; The formula for calculating word pair probability is as follows: ; in, Represents a set of vocabulary pairs Chinese word pair Become the first The probability of the topic of historical medical literature data after group processing, Indicated in Word pairs in historical medical literature data after group processing Number of occurrences; S23, based on the calculated probability of each word pair becoming a topic, constructing a document topic analysis model to determine the topic of the processed historical medical document data; The formula for calculating word pair weights is as follows: ; in, Representing word pairs The weight of represents the probability parameter, Represents positional parameters, Representing word pairs The location in the historical medical literature data, including: title, subtitle, text, abstract, etc.; Further, the word pair probability calculation formula and the word pair weight calculation formula are combined, and the combined word pair probability calculation formula and the word pair weight calculation formula are set as the literature topic analysis model; Setting a weight threshold of word pairs in the literature topic analysis model, summarizing word pairs whose weights are greater than or equal to the weight threshold, and determining the processed historical medical literature data topics based on the summarized word pairs; For further information, see Figure 1 , analyzing and training the processed historical medical literature data themes through intelligent analysis methods, and obtaining the trained historical medical literature data theme set includes the following steps: S31, selecting some of the processed historical medical literature data topics from the determined processed historical medical literature data topics to construct a training set; S32, inputting the themes of the historical medical literature data processed in the training set into the BP neural network, and determining the BP neural network model through iterative training; The themes of historical medical literature data processed in the training set are input into the BP neural network. The structure of the BP neural network includes: input layer, hidden layer and output layer; Set the input set of the input layer to ,in Indicates the input The total number of historical medical literature data themes processed in the input training set is ; The output set of the output layer is ,in Indicates the output The total number of historical medical literature data topics after training is output as ; Assume that the hidden layer contains q neurons, v is the weight from the input layer to the hidden layer, and w is the weight from the hidden layer to the output layer; Training is performed through the forward propagation process of the BP neural network; During the forward propagation process, the calculation formula for transferring data from the input layer to the hidden layer is: ; in, represents the input of the hth hidden layer neuron, represents the weight of the i-th input of the input layer to the h-th hidden layer neuron, and m represents the number of input data of the input layer; Represents the bias value of the hth hidden layer neuron; The calculation formula for data transmission from the hidden layer to the output layer is: ; in, represents the input of the jth output layer neuron, represents the weight from the hth hidden layer neuron to the jth output layer neuron, and q represents the number of hidden layer neurons; represents the output of the hth hidden layer neuron; Represents the bias value of the jth output layer neuron; After receiving the parameter data transmitted by the hidden layer, the output layer directly outputs the parameter data received from the hidden layer; Right now ; Calculate the error between the output layer and the expected value; Set the error threshold between the output layer and the expected value. When the error between the output layer and the expected value is greater than or equal to the set error threshold, iterative calculation is performed by adjusting the weights from the hidden layer to the output layer and the weights from the input layer to the hidden layer in turn until a BP neural network model is obtained. The iterative calculation formula is as follows: ; in, represents the weight adjustment value, represents the learning rate, Indicates the error, Represents the output of the output layer; S33, based on the determined BP neural network model, training all processed historical medical literature data themes, and outputting a trained historical medical literature data theme set; For further information, see Figure 1 , collect medical literature data in real time, determine the theme of the medical literature data collected in real time based on the constructed literature theme analysis model, and analyze and match the theme set of historical medical literature data after training through natural language processing, and obtain the theme of the analyzed historical medical literature data, including the following steps: Collect medical literature data in real time, determine the theme of the medical literature data collected in real time based on the constructed literature theme analysis model, and calculate the similarity between the theme of the medical literature data collected in real time and the theme set of historical medical literature data after training through the Jaccard coefficient; The calculation formula is: ; Among them, KT is the real-time collection of medical literature data themes, KN ​​is the training of historical medical literature data themes, Represents the similarity between set KT and set KN; Set the Jaccard similarity threshold, and perform analysis and matching through similarity matching. When the similarity between the real-time collected medical literature data theme and the trained historical medical literature data theme is greater than or equal to the threshold, it is determined that the real-time collected medical literature data theme is related to the trained historical medical literature data theme. If the similarity is lower than the threshold, it is determined that the real-time collected medical literature data theme is not related to the trained historical medical literature data theme. Furthermore, the relevant medical literature data were combined and submitted to manual review; Example 2 See also Figure 1 , this embodiment also discloses a medical literature data intelligent analysis system based on natural language processing, which is used to implement a medical literature data intelligent analysis method based on natural language processing, and the system includes: a data acquisition module, a data processing module, an analysis and training module, and a natural language processing module; The data acquisition module is used to receive medical literature data in real time; The data processing module is used to process the received medical literature data and transmit the processed medical literature data to the analysis and training module; The analysis and training module is used to analyze the received processed medical literature data; The natural language processing module is used to determine real-time medical literature data by calculating relevance.

[0027] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent analysis of medical literature data based on natural language processing, characterized in that: The following steps are involved: S1. Collecting historical medical literature data, processing the collected historical medical literature data to obtain processed historical medical literature data, and constructing a sample set based on the processed historical medical literature data; S2. Based on the historical medical literature data processed in the sample set, a literature theme analysis model is constructed, and the theme of the processed historical medical literature data is determined based on the constructed literature theme analysis model; S3, analyzing and training the processed historical medical literature data themes through intelligent analysis methods to obtain a trained historical medical literature data theme set; S4. Collect medical literature data in real time, determine the theme of the medical literature data collected in real time based on the constructed literature theme analysis model, and at the same time, analyze and match the theme set of historical medical literature data after training through natural language processing to obtain the analyzed theme of historical medical literature data.

2. According to claim 1, a method for intelligent analysis of medical literature data based on natural language processing is characterized in that: The collecting of historical medical literature data, processing the collected historical medical literature data to obtain processed historical medical literature data, and constructing a sample set based on the obtained processed historical medical literature data comprises the following steps: S11, collecting historical medical literature data, filtering and processing the collected historical medical literature data to obtain processed historical medical literature data; S12, constructing a sample set based on the processed historical medical literature data; Building a sample set ; in, represents the sample set, represents the first set of processed historical medical literature data in the sample set, The nth group of processed historical medical literature data in the sample set.

3. The method for intelligent analysis of medical literature data based on natural language processing according to claim 2, characterized in that: The filtering process of the collected historical medical literature data to obtain the processed historical medical literature data comprises the following steps: Create a length of Array, select A hash function is used to calculate the name and time of each collected historical medical literature data, and the calculation results are stored in an array; in, represents the number of hash functions, Indicates the amount of historical medical literature data collected; In the process of calculating the collected historical medical literature data using the hash function, when the calculation results of two sets of historical medical literature data are consistent, the names and times in the two sets of historical medical literature data are compared; When the comparison results are consistent, the current two sets of historical medical literature data are set as duplicate historical medical literature data; When two sets of historical medical literature data are duplicate historical medical literature data, one set of historical medical literature data is deleted to implement filtering processing; After the calculation is completed, the historical medical literature data stored in the array during the calculation process is summarized to obtain the processed historical medical literature data.

4. The method for intelligent analysis of medical literature data based on natural language processing according to claim 1, characterized in that: The method of constructing a document theme analysis model based on the processed historical medical document data in the sample set, and determining the theme of the processed historical medical document data based on the constructed document theme analysis model comprises the following steps: S21. Randomly select a set of processed historical medical literature data from the sample set, extract all the word pairs, and construct a word pair set ; S22. For vocabulary pair sets For each word pair in , calculate the probability that the word pair becomes the subject of the processed historical medical literature data; ； in, Represents a set of vocabulary pairs Chinese word pair Become the first The probability of the topic of historical medical literature data after group processing, Indicated in Word pairs in historical medical literature data after group processing Number of occurrences; S23. Based on the calculated probability of each word pair becoming a topic, a document topic analysis model is constructed to determine the topic of the processed historical medical document data.

5. The method for intelligent analysis of medical literature data based on natural language processing according to claim 4, characterized in that: The method of constructing a document topic analysis model based on the calculated probability of each word pair becoming a topic and determining the topic of the processed historical medical document data comprises the following steps: The formula for calculating word pair weights is as follows: ； in, Representing word pairs The weight of represents the probability parameter, Represents positional parameters, Representing word pairs Location in historical medical literature data, including title, subheadings, text, and abstract; Combine the word pair probability calculation formula and the word pair weight calculation formula, and set the combined word pair probability calculation formula and word pair weight calculation formula as the literature topic analysis model; The weight threshold of the word pairs in the literature topic analysis model is set, the word pairs with weights greater than or equal to the weight threshold are summarized, and the processed historical medical literature data topics are determined based on the summarized word pairs.

6. The method for intelligent analysis of medical literature data based on natural language processing according to claim 1, characterized in that: The method of analyzing and training the processed historical medical literature data themes by the intelligent analysis method to obtain the trained historical medical literature data theme set comprises the following steps: S31, selecting some of the processed historical medical literature data topics from the determined processed historical medical literature data topics to construct a training set; S32, inputting the themes of the historical medical literature data processed in the training set into the BP neural network, and determining the BP neural network model through iterative training; S33. Based on the determined BP neural network model, all processed historical medical literature data topics are trained, and a set of trained historical medical literature data topics is output.

7. The method for intelligent analysis of medical literature data based on natural language processing according to claim 6, characterized in that: The process of inputting the historical medical literature data themes processed in the training set into the BP neural network and determining the BP neural network model by iterative training includes the following steps: The themes of historical medical literature data processed in the training set are input into the BP neural network, and the structure of the BP neural network includes an input layer, a hidden layer and an output layer; Set the input set of the input layer to ,in Indicates the input The total number of historical medical literature data themes processed in the input training set is ; The output set of the output layer is ,in Indicates the output The total number of historical medical literature data topics after training is output as ; Assume that the hidden layer contains q neurons, and w is the weight from the hidden layer to the output layer; Training is performed through the forward propagation process of the BP neural network; During the forward propagation process, the calculation formula for transferring data from the input layer to the hidden layer is: ； in, represents the input of the hth hidden layer neuron, represents the weight of the i-th input of the input layer to the h-th hidden layer neuron, and m represents the number of input data of the input layer; Represents the bias value of the hth hidden layer neuron; The calculation formula for data transmission from the hidden layer to the output layer is: ； in, represents the input of the jth output layer neuron, represents the weight from the hth hidden layer neuron to the jth output layer neuron, and q represents the number of hidden layer neurons; represents the output of the hth hidden layer neuron; represents the bias value of the jth output layer neuron; After receiving the parameter data transmitted by the hidden layer, the output layer directly outputs the parameter data received from the hidden layer; Right now ; Calculate the error between the output layer and the expected value; Set the error threshold between the output layer and the expected value. When the error between the output layer and the expected value is greater than or equal to the set error threshold, iterative calculation is performed by adjusting the weights from the hidden layer to the output layer and the weights from the input layer to the hidden layer in turn until a BP neural network model is obtained. The iterative calculation formula is as follows: ； in, represents the weight adjustment value, represents the learning rate, Indicates the error, Represents the output of the output layer.

8. The method for intelligent analysis of medical literature data based on natural language processing according to claim 1, characterized in that: The real-time collection of medical literature data, determining the theme of the real-time collected medical literature data based on the constructed literature theme analysis model, and simultaneously analyzing and matching the obtained trained historical medical literature data theme set through natural language processing to obtain the analyzed historical medical literature data theme include the following steps: Collect medical literature data in real time, determine the theme of the medical literature data collected in real time based on the constructed literature theme analysis model, and calculate the similarity between the theme of the medical literature data collected in real time and the theme of the historical medical literature data after training through the Jaccard coefficient; The calculation formula is: ； Among them, KT is the real-time collection of medical literature data themes, KN ​​is the training of historical medical literature data themes, Represents the similarity between set KT and set KN; The Jaccard similarity threshold is set, and analysis and matching are performed through similarity matching. When the similarity between the real-time collected medical literature data theme and the trained historical medical literature data theme is greater than or equal to the threshold, it is determined that the real-time collected medical literature data theme is related to the trained historical medical literature data theme. If the similarity is lower than the threshold, it is determined that the real-time collected medical literature data theme is not related to the trained historical medical literature data theme.

9. A system for implementing the medical literature data intelligent analysis method based on natural language processing according to any one of claims 1 to 8, characterized in that: include: Data collection module, data processing module, analysis and training module, and natural language processing module; The data acquisition module is used to receive medical literature data in real time; The data processing module is used to process the received medical literature data and transmit the processed medical literature data to the analysis and training module; The analysis and training module is used to analyze the received processed medical literature data; The natural language processing module is used to determine real-time medical literature data by calculating relevance.

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