Text feature data processing method and system based on BERT model and TF-IDF weighting

Abstract

The present invention belongs to the technical field of intelligent data processing, and specifically relates to a text feature data processing method and system based on the BERT model and TF‑IDF weighting. The method first obtains the data to be analyzed of the target field text, and uses the jieba library to perform preprocessing such as word segmentation, removal of stop words, and unification of text format. Subsequently, an effect dictionary is constructed, including the construction of an initial effect word dictionary, dictionary expansion, and manual inspection and annotation. In the feature extraction stage, the TF‑IDF value is calculated to evaluate the importance of words in the document. In the innovation scoring link, the effect words are matched by regular expressions, and a large language model is used for secondary judgment, and the common effect words are secondary weighted, and finally the innovation of the target field text is scored based on the TF‑IDF value. This method combines the deep semantic understanding of the BERT model with the text statistical characteristics of TF‑IDF, which improves the accuracy and efficiency of the innovation evaluation of the target field text.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent data processing technology, and specifically relates to a text feature data processing method and system based on the BERT model and TF-IDF weighting. Background Art

[0002] In the current technological innovation landscape, the number and quality of a company's patents are crucial indicators of its innovation capabilities. However, traditional manual evaluation methods are not only time-consuming and labor-intensive, but also susceptible to subjective factors, making accuracy and consistency difficult to guarantee. Advances in natural language processing technology, particularly pre-trained language models like BERT and text feature extraction methods like TF-IDF, have provided new technical tools for evaluating patent innovation.

[0003] While some patent analysis tools based on natural language processing exist, these tools often focus solely on simple statistical features or keyword matching within patent text, lacking in-depth semantic understanding and comprehensive evaluation models. This results in inaccurate and incomplete evaluation results. Therefore, it is necessary to develop an innovativeness evaluation system and method that combines the BERT model with TF-IDF weighted scoring to achieve a more accurate and efficient assessment of the innovativeness of patent abstracts. Summary of the Invention

[0004] The purpose of the present invention is to provide a text feature data processing method and system based on the BERT model and TF-IDF weighting to solve the problems raised in the background technology.

[0005] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0006] In a first aspect, the present invention proposes a text feature data processing method based on the BERT model and TF-IDF weighting, the method comprising:

[0007] Obtain the text data to be analyzed of the target domain text;

[0008] Performing preprocessing on the text data to be analyzed by segmenting words, removing stop words, and unifying the text format to generate text data to be processed;

[0009] After inputting the text data to be processed into a pre-built effect dictionary for effect word matching and evaluation, a large language model is used to perform secondary judgment and secondary weighting on the matched effect words to generate the text feature data processing results of the target domain text;

[0010] The pre-built effect dictionary includes initial effect words, words obtained by co-occurrence analysis, synonyms obtained by word vector model, and an effect dictionary of newly marked words.

[0011] Furthermore, the preprocessing of segmenting the text data to be analyzed, removing stop words, and unifying the text format includes:

[0012] Use the jieba library to segment the text data to be analyzed;

[0013] Prepare a stop word list, including common conjunctions, prepositions, etc., use jieba to segment words and filter stop words;

[0014] Cleans punctuation and invalid characters, retaining characters that are useful for analysis.

[0015] Furthermore, the pre-built effect dictionary includes initial effect words, words obtained from co-occurrence analysis, synonyms obtained from the word vector model, and an effect dictionary of newly annotated words, including:

[0016] Construct and optimize a dictionary of key words that describe the effects within the target domain text, including the construction of an initial dictionary of effect words, dictionary expansion, and manual inspection and annotation after the fusion of the dictionary;

[0017] The dictionary expansion is specifically achieved through co-occurrence analysis, training word vector models, and using the BERT model to perform contextual analysis of words;

[0018] The co-occurrence analysis includes collecting corpus, calculating co-occurrence frequency and screening co-occurring words;

[0019] The training word vector model includes using the transformers library to load the pre-trained BERT model to convert each word into a vector representation and calculate semantic similarity. The context analysis includes constructing a sentence containing the initial effect word.

[0020] The BERT model is used to encode sentences containing initial effect words, obtain contextual representations of the words, and then analyze the contextual representations to identify changes in the meanings of words in different contexts, and add new annotation information to the initial effect words.

[0021] Furthermore, the co-occurrence analysis specifically includes the following steps:

[0022] Collect corpus and perform word segmentation on the text in the corpus;

[0023] For each initial effect word, count the number of times it co-occurs with other words within a set number of word windows, create a co-occurrence matrix, and record the co-occurrence frequency of each word pair;

[0024] Set a threshold and filter out words with a co-occurrence frequency higher than the threshold as potential effect word candidates.

[0025] Furthermore, the training word vector model specifically includes the following steps:

[0026] Use the transformers library to load the pre-trained BERT model and convert each word into a vector representation;

[0027] For each initial effect word, calculate its cosine similarity with all other words in the dictionary, sort the similarity results, and select the n words with the highest similarity as synonyms.

[0028] Furthermore, the step of inputting the to-be-processed text data into a pre-built effect dictionary for effect word matching and evaluation includes:

[0029] Calculate the TF-IDF value of each word in the text data to be processed;

[0030] Effect word matching and scoring are performed based on the position and TF-IDF value of the words in the effect dictionary in the text data to be processed.

[0031] Furthermore, the use of the large language model to perform secondary judgment and secondary weighting on the matched effect words to generate text feature data processing results of the target domain text includes:

[0032] Based on the words in the effect dictionary, a regular expression matching pattern is constructed to locate the effect words in the text data to be processed;

[0033] Input the sentence containing the effect word into the large language model for secondary judgment to obtain the inferred effect word;

[0034] Compare the inference effect words output by the large language model with the effect words matched by the regular expression, and perform secondary weighting on the effect words that exist in both the inference results and the matching results;

[0035] Based on the TF-IDF value and combined with the effect word score after secondary empowerment, the innovation of text in a single target field is scored.

[0036] Furthermore, the secondary weighted score is calculated as follows:

[0037] Set the weight coefficient α, for each common effect word t commonk , the effect word score is calculated as the original TF-IDF value multiplied by the weighting coefficient α.

[0038] Furthermore, the evaluation of the innovation of the text in a single target field is calculated according to the following formula:

[0039] The text summary D contains t effect words {t1, t2, ..., t m}, the innovation score of the target field text is:

[0040]

[0041] Among them, f commoni is a common effect word t commoni The number of times it appears in the data to be analyzed D; f non-commonj Yes or No Common Effect Words non-commonj The number of occurrences; m is the total number of effect words matched by the regular expression, k is the number of common effect words, and mk is the number of non-common effect words.

[0042] In a second aspect, the present invention proposes a text feature data processing system based on the BERT model and TF-IDF weighting, characterized in that it is applied to execute any of the above-mentioned data processing methods, and the system includes:

[0043] A data acquisition module is used to obtain the text data to be analyzed in the target field text;

[0044] A data preprocessing module is used to preprocess the text data to be analyzed by segmenting the words, removing stop words and unifying the text format to generate text data to be processed;

[0045] A matching analysis module is used to input the text data to be processed into a pre-built effect dictionary to match and evaluate effect words, and then use a large language model to perform secondary judgment and secondary weighting on the matched effect words to generate text feature data processing results of the target domain text;

[0046] The pre-built effect dictionary includes initial effect words, words obtained by co-occurrence analysis, synonyms obtained by word vector model, and an effect dictionary of newly marked words.

[0047] The beneficial effects of the present invention are:

[0048] 1. By combining the BERT model and TF-IDF weighted scoring, this method can deeply explore the key effect words in the text and accurately assess their importance. The application of the BERT model enables a deeper understanding of the word context, effectively avoiding the misjudgment caused by simple matching or statistics in traditional methods. By constructing an enhanced effect dictionary containing initial effect words, co-occurrence analysis words, word vector model synonyms, and newly annotated words, this method covers a wider range of effect words and improves the comprehensiveness of the evaluation.

[0049] 2. This method provides an objective and scientific basis for evaluating patent innovation through the quantitative TF-IDF value and the secondary weighted innovation score. This helps businesses, research institutions, and individuals make more informed decisions during the patent application, examination, and maintenance processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A flowchart of a text feature data processing method based on the BERT model and TF-IDF weighting provided in an embodiment of the present application;

[0051] Figure 2 A schematic diagram of the structure of a text feature data processing system based on the BERT model and TF-IDF weighting provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] The present application is described in further detail below in conjunction with the accompanying drawings. It is necessary to point out that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technicians in this field can make some non-essential improvements and adjustments to the present application based on the above application content.

[0053] Example 1

[0054] like Figure 1 As shown, this embodiment proposes a text feature data processing method based on the BERT model and TF-IDF weighting, including the following steps:

[0055] S1. Obtain the text data to be analyzed in the target field; how to identify target companies, access patent databases (such as USPTO, EPO, CNIPA, etc.), and use API to extract patent abstracts.

[0056] S2. Preprocess the text data to be analyzed by segmenting, removing stop words and unifying the text format to generate the text data to be processed

[0057] S3. After the text data to be processed is input into the pre-built effect dictionary for effect word matching and evaluation, the large language model is used to perform secondary judgment and secondary weighting on the matched effect words to generate the text feature data processing results of the target field text;

[0058] The pre-built effect dictionary includes initial effect words, words obtained by co-occurrence analysis, synonyms obtained by word vector model, and an effect dictionary of newly marked words.

[0059] Further preferably, the preprocessing of segmenting the text data to be analyzed, removing stop words, and unifying the text format includes:

[0060] Use the jieba library to segment the text data to be analyzed;

[0061] Prepare a stop word list, including common conjunctions, prepositions, etc., use jieba to segment words and filter stop words;

[0062] Cleans punctuation and invalid characters, retaining characters that are useful for analysis.

[0063] 6. Further preferably, the pre-built effect dictionary includes initial effect words, words obtained by co-occurrence analysis, synonyms obtained by word vector model, and an effect dictionary of newly marked words, including:

[0064] Construct and optimize a dictionary of key words that describe the effects within the target domain text, including the construction of an initial dictionary of effect words, dictionary expansion, and manual inspection and annotation after the fusion of the dictionary;

[0065] The dictionary expansion is specifically achieved through co-occurrence analysis, training word vector models, and using the BERT model to perform contextual analysis of words;

[0066] The co-occurrence analysis includes collecting corpus, calculating co-occurrence frequency and screening co-occurring words;

[0067] The training word vector model includes using the transformers library to load the pre-trained BERT model to convert each word into a vector representation and calculate semantic similarity. The context analysis includes constructing a sentence containing the initial effect word.

[0068] The BERT model is used to encode sentences containing initial effect words, obtain contextual representations of the words, and then analyze the contextual representations to identify changes in the meanings of words in different contexts, and add new annotation information to the initial effect words.

[0069] Further preferably, the co-occurrence analysis specifically includes the following steps:

[0070] Collect corpus and perform word segmentation on the text in the corpus;

[0071] For each initial effect word, count the number of times it co-occurs with other words within a set number of word windows, create a co-occurrence matrix, and record the co-occurrence frequency of each word pair;

[0072] Set a threshold and filter out words with a co-occurrence frequency higher than the threshold as potential effect word candidates.

[0073] Further preferably, the training word vector model specifically includes the following steps:

[0074] Use the transformers library to load the pre-trained BERT model and convert each word into a vector representation;

[0075] For each initial effect word, calculate its cosine similarity with all other words in the dictionary, sort the similarity results, and select the n words with the highest similarity as synonyms.

[0076] Further preferably, the step of inputting the to-be-processed text data into a pre-built effect dictionary for effect word matching and evaluation includes:

[0077] Calculate the TF-IDF value of each word in the text data to be processed;

[0078] Effect word matching and scoring are performed based on the position and TF-IDF value of the words in the effect dictionary in the text data to be processed.

[0079] Further preferably, the use of the large language model to perform secondary judgment and secondary weighting on the matched effect words to generate text feature data processing results of the target domain text includes:

[0080] Based on the words in the effect dictionary, a regular expression matching pattern is constructed to locate the effect words in the text data to be processed;

[0081] Input the sentence containing the effect word into the large language model for secondary judgment to obtain the inferred effect word;

[0082] Compare the inference effect words output by the large language model with the effect words matched by the regular expression, and perform secondary weighting on the effect words that exist in both the inference results and the matching results;

[0083] Based on the TF-IDF value and combined with the effect word score after secondary empowerment, the innovation of a single public text is scored.

[0084] More preferably, the secondary weighted score is calculated as follows:

[0085] Set the weight coefficient α, for each common effect word t commonk , the effect word score is calculated as the original TF-IDF value multiplied by the weighting coefficient α.

[0086] Further preferably, the evaluation of the innovation of the text in a single target field is calculated according to the following formula:

[0087] The text summary D contains t effect words {t1, t2, ..., t m}, the innovation score of the target field text is:

[0088]

[0089] Among them, f commoni is a common effect word t commoni The number of times it appears in the abstract text D; f non-commonj Yes or No Common Effect Words non-commonj The number of occurrences; m is the total number of effect words matched by the regular expression, k is the number of common effect words, and mk is the number of non-common effect words.

[0090] It should be noted that the text feature data processing method based on the BERT model and TF-IDF weighting proposed in this embodiment can also be applied to the content feature extraction of patent texts, thereby improving the evaluation of innovation.

[0091] As an example, the innovation evaluation according to the above embodiment specifically includes the following steps:

[0092] 1. Data Acquisition

[0093] 1. Obtain all patent summaries of enterprises in the target field:

[0094] Identify target companies, access patent databases (such as USPTO, EPO, CNIPA, etc.), and use APIs to extract patent abstracts.

[0095] 2. Data Preprocessing

[0096] Use the jieba library to segment the patent abstract text:

[0097] ①Word segmentation optimization:

[0098] Collect industry-related professional effect vocabulary and use jieba.load_userdict() to load a custom dictionary.

[0099] ②Remove stop words:

[0100] Prepare a stop word list, including common conjunctions, prepositions, etc. At the same time, optimize the dictionary based on the original stop word dictionary to ensure the effectiveness of the word segmentation results, use jieba word segmentation, and filter stop words.

[0101] ③ Unified text format:

[0102] Cleans punctuation and invalid characters, retaining characters that are useful for analysis.

[0103] Build effect dictionary

[0104] 3. Construction of the initial effect word dictionary:

[0105] Identify key words that describe effects in the field, such as "high efficiency", "energy saving", "improvement", etc., summarize these words and form an initial effect word dictionary.

[0106] 2. Dictionary expansion:

[0107] ① Co-occurrence analysis:

[0108] Use statistical methods to calculate word co-occurrence frequencies and identify words that co-occur with known effect words.

[0109] a. Collect corpus: Based on the target domain, collect and organize text corpus and perform word segmentation on the text in the corpus to identify words.

[0110] b. Calculate co-occurrence frequency: For each initial effect word, count the number of times it co-occurs with other words within the 5-word window, create a co-occurrence matrix, and record the co-occurrence frequency of each word pair.

[0111] d. Filter co-occurring words: Set a threshold and filter out words with a co-occurrence frequency higher than the threshold as potential effect word candidates.

[0112] ②Training word vector model:

[0113] a. Training word vector model:

[0114] Use the transformers library to load the pre-trained BERT model and convert each word into a vector representation through the BERT model.

[0115] b. Calculate semantic similarity:

[0116] For each initial effect word, calculate its cosine similarity with all other words in the dictionary and sort the similarity results.

[0117] The cosine similarity is calculated using the following formula:

[0118]

[0119] Select the n words with the highest similarity as synonyms.

[0120] ③ Use the BERT model to perform contextual analysis of words, and then combine the initial dictionary and newly annotated words to generate an enhanced effect dictionary.

[0121] a. Contextual Analysis:

[0122] Construct sentences containing initial effect words, ensure that the words appear in different contexts, and use the BERT model to encode these sentences to obtain the contextual representation of the words.

[0123] b. New word annotation:

[0124] Analyze the context representation, identify the changes in the meaning of words in different contexts, and add new annotation information to the initial effect words based on the analysis results.

[0125] 3. Fusion Dictionary:

[0126] Merge the initial dictionary, the words obtained from the co-occurrence analysis, the synonyms obtained from the word vector model, and the newly annotated words.

[0127] 4. Manual inspection and annotation:

[0128] The merged dictionary is deduplicated and verified to ensure its quality, and the validity and relevance of words are confirmed based on domain knowledge.

[0129] 4. Feature Extraction

[0130] 1. Calculate TF-IDF

[0131] ①Calculate the TF (word frequency) of the word:

[0132]

[0133] ②Calculate the IDF (Inverse Document Frequency) of the word:

[0134]

[0135] ③Calculate TF-IDF:

[0136] TF-IDF(t,d)=TF(t,d)×IDF(t)

[0137] 2. Effective word scoring:

[0138] The words in the document are sorted according to the TF-IDF value. The words with higher scores are considered to be more discriminative or important. The score of the effect word is TF-IDF(t i ,D).

[0139] 5. Innovation Scoring

[0140] 1. Effect word matching

[0141] Based on the words in the effect dictionary, a regular expression matching pattern is constructed to locate the effect words in the abstract text, obtain all the effect words and sentences containing effect words in patent abstract D, and assign a score to each matched effect word based on the previously calculated TF-IDF value.

[0142] 2. Input the sentence containing the effect word into the large language model, and make a secondary judgment on the effect word with the help of the large language model to obtain the inferred effect words {w1, w2, w3, ..., w n}, compare the inference effect words output by the large language model with the effect words matched by the regular expression, and perform the comparison on the effect words {t common1 ,t common2 ,…,t commonk}Carry out secondary empowerment.

[0143] Based on the accuracy of large language models, they generally outperform simple regular expression matching in natural language processing tasks. Furthermore, the presence of common effect words in both judgments indicates that these words are highly important in patent abstracts and contribute significantly to the patent's innovativeness. Therefore, effect words identified by large language models are more credible and should be given higher weight.

[0144] Set the weighting coefficient α = 1.5, for each common effect word t commonk , whose score is calculated as:

[0145] TF-IDF(t commoni ,D)×α

[0146] 3. Based on the TF-IDF value, score the innovation of each patent after secondary weighting:

[0147] Patent abstract D, contains t effect words {t1, t2, ..., t m}, the innovation score of patent claim D is:

[0148]

[0149] Among them, f commoni is a common effect word t commoni The number of times it appears in the abstract text D; f non-commonj Yes or No Common Effect Words non-commonj The number of occurrences; m is the total number of effect words matched by the regular expression, k is the number of common effect words, and mk is the number of non-common effect words.

[0150] According to the above embodiment of the present invention, the solution can first be implemented by obtaining all patent abstracts of the target enterprise from the patent database, which serves as the basic data for evaluation. Subsequently, the patent abstracts are segmented using the Jieba library, and stop words are removed and the text format is standardized to ensure data quality and analysis effectiveness.

[0151] Next, this application constructs an effect dictionary, which not only contains the initial effect words, but also expands and optimizes it through methods such as co-occurrence analysis, word vector model, and context analysis of the BERT model, so as to more comprehensively cover the effect words in the target field.

[0152] During the feature extraction stage, this application calculated the TF-IDF value of each word to evaluate its importance and distinctiveness in the patent abstract.

[0153] Finally, this application uses the constructed effect dictionary and TF-IDF values ​​to match and score effect terms in patent abstracts. Furthermore, a large language model is introduced to perform a secondary evaluation of effect terms, further improving the accuracy of the evaluation. Through comprehensive calculations, a patent's innovativeness score is derived, providing strong support for the evaluation of patent technology.

[0154] Based on the same inventive concept, Figure 2 As shown, this embodiment also proposes a text feature data processing system based on the BERT model and TF-IDF weighting, which is applied to execute the above data processing method. The system includes:

[0155] A data acquisition module is used to obtain summary texts of public texts in the target field;

[0156] A data preprocessing module, configured to perform word segmentation, remove stop words, and preprocess the summary text into a unified text format;

[0157] A matching analysis module is used to input the text data to be processed into a pre-built effect dictionary to match and evaluate effect words, and then use a large language model to perform secondary judgment and secondary weighting on the matched effect words to generate text feature data processing results of the target domain text;

[0158] The pre-built effect dictionary includes initial effect words, words obtained by co-occurrence analysis, synonyms obtained by word vector model, and an effect dictionary of newly marked words.

[0159] It should be noted here that each module in the above-mentioned data processing system corresponds to steps S1 to S3 in implementing the above-mentioned data processing method, and the instances and application scenarios implemented by multiple modules and corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned embodiment 1.

[0160] This embodiment further provides an electronic device, including:

[0161] a processor; a memory for storing instructions executable by the processor;

[0162] The processor is configured to execute instructions to implement the above-mentioned text feature data processing method based on the BERT model and TF-IDF weighting.

[0163] This embodiment also proposes a computer-readable storage medium. When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device can perform the text feature data processing method based on the BERT model and TF-IDF weighting as described above.

[0164] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0165] In addition, the functional modules in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0166] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0167] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A text feature data processing method based on the BERT model and TF-IDF weighting, characterized in that: The method comprises: Obtain the text data to be analyzed of the target domain text; Performing preprocessing on the text data to be analyzed by segmenting words, removing stop words, and unifying the text format to generate text data to be processed; After inputting the text data to be processed into a pre-built effect dictionary for effect word matching and evaluation, a large language model is used to perform secondary judgment and secondary weighting on the matched effect words to generate the text feature data processing results of the target domain text; The pre-built effect dictionary includes initial effect words, words obtained by co-occurrence analysis, synonyms obtained by word vector model, and an effect dictionary of newly marked words; The method of using the large language model to perform secondary judgment and secondary weighting on the matched effect words to generate text feature data processing results of the target domain text includes: Based on the words in the effect dictionary, a regular expression matching pattern is constructed to locate the effect words in the text data to be processed; Input the sentence containing the effect word into the large language model for secondary judgment to obtain the inferred effect word; Compare the inference effect words output by the large language model with the effect words matched by the regular expression, and perform secondary weighting on the effect words that exist in both the inference results and the matching results; Based on the TF-IDF value of each word in the text data to be processed and combined with the score of the effect word after secondary empowerment, the innovation of the text in a single target field is scored; The secondary weighted score is calculated as follows: Set the weighting coefficient , for each common effect word The effect word score is calculated as the original TF-IDF value multiplied by the weighting coefficient ; The scoring of the innovation of text in a single target field is calculated according to the following formula: Text Summarization Include effect words , the innovation score of the target field text is: ； in, is a common effect word Data to be analyzed The number of times it appears in Yes and No Common Effect Words Number of occurrences; is the total number of effect words matched by the regular expression, is the number of common effect words, is the number of non-common effect words.

2. According to claim 1 Model and The weighted text feature data processing method is characterized by: The preprocessing of segmenting the text data to be analyzed, removing stop words, and unifying the text format includes: Use the jieba library to segment the text data to be analyzed; Prepare a stop word list, including common conjunctions and prepositions, use jieba to segment words and filter stop words; Cleans punctuation and invalid characters, retaining characters that are useful for analysis.

3. The text feature data processing method based on the BERT model and TF-IDF weighting according to claim 1, characterized in that: The pre-built effect dictionary includes initial effect words, words obtained from co-occurrence analysis, synonyms obtained from the word vector model, and an effect dictionary of newly annotated words, including: Construct and optimize a dictionary of key words that describe the effects within the target domain text, including the construction of an initial dictionary of effect words, dictionary expansion, and manual inspection and annotation after the fusion of the dictionary; The dictionary expansion is specifically achieved through co-occurrence analysis, training word vector models, and using the BERT model to perform contextual analysis of words; The co-occurrence analysis includes collecting corpus, calculating co-occurrence frequency and screening co-occurring words; The training word vector model includes using the transformers library to load the pre-trained BERT model to convert each word into a vector representation and calculate semantic similarity. The context analysis includes constructing a sentence containing the initial effect word. The BERT model is used to encode sentences containing initial effect words, obtain contextual representations of the words, and then analyze the contextual representations to identify changes in the meanings of words in different contexts, and add new annotation information to the initial effect words.

4. The text feature data processing method based on the BERT model and TF-IDF weighting according to claim 3, characterized in that: The co-occurrence analysis specifically includes the following steps: Collect corpus and perform word segmentation on the text in the corpus; For each initial effect word, count the number of times it co-occurs with other words within a set number of word windows, create a co-occurrence matrix, and record the co-occurrence frequency of each word pair; Set a threshold and filter out words with a co-occurrence frequency higher than the threshold as potential effect word candidates.

5. The text feature data processing method based on the BERT model and TF-IDF weighting according to claim 3, characterized in that: The training word vector model specifically includes the following steps: Use the transformers library to load the pre-trained BERT model and convert each word into a vector representation; For each initial effect word, calculate its cosine similarity with all other words in the dictionary, sort the similarity results, and select the n words with the highest similarity as synonyms.

6. The text feature data processing method based on the BERT model and TF-IDF weighting according to claim 1, characterized in that: The step of inputting the to-be-processed text data into a pre-built effect dictionary for effect word matching and evaluation includes: Calculate the TF-IDF value of each word in the text data to be processed; Effect word matching and scoring are performed based on the position and TF-IDF value of the words in the effect dictionary in the text data to be processed.

7. A text feature data processing system based on the BERT model and TF-IDF weighting, characterized by: Applied to executing the data processing method according to any one of claims 1 to 6, the system comprises: A data acquisition module is used to obtain the text data to be analyzed in the target field text; A data preprocessing module is used to preprocess the text data to be analyzed by segmenting the words, removing stop words and unifying the text format to generate text data to be processed; A matching analysis module is used to input the text data to be processed into a pre-built effect dictionary to match and evaluate effect words, and then use a large language model to perform secondary judgment and secondary weighting on the matched effect words to generate text feature data processing results of the target domain text; The pre-built effect dictionary includes initial effect words, words obtained by co-occurrence analysis, synonyms obtained by word vector model, and an effect dictionary of newly marked words.

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