8 results about "Text mining" patented technology

The method for automatic identification, classification and development trend analysis of net red villages based on multi-source data fusion and natural language processing comprises the following steps: UGC data is crawled from Xiaohongshu and Douyin through a distributed master-slave architecture, de-duplicated based on SimHash, and normalized in time and coding format; a text semantic fingerprint is generated, and multi-level semantic cache fingerprint matching is performed; for unassigned text, its complexity is calculated, and a large language model API is adaptively called to automatically complete and extract five-level administrative divisions; weights are determined based on the analytic hierarchy process, interaction indicators such as likes, comments, collections and forwards are integrated, and a comprehensive network heat index of the village is obtained; an external text mining tool is connected, and batch word frequency analysis, semantic network analysis and sentiment tendency evaluation are performed; a document-term matrix is constructed, TF-IDF weighting is performed, and unsupervised clustering algorithm is used for clustering analysis of village characteristics; cross-dimension analysis is performed on the clustering results, and a development portrait, advantage mining and operation suggestion warning are automatically generated in combination with the SWOT model.

This invention relates to a method and system for mining research hotspots in universities based on an improved BERTopic, belonging to the field of text mining technology. It includes the following steps: Step S1, obtaining a list of effective word segments from university research papers; Step S2, capturing the core semantic information of academic texts; Step S3, obtaining a 3D low-dimensional vector that retains the core semantic features; Step S4, identifying potential research hotspot topic clusters; Step S5, generating a set of university research hotspot topics containing core keywords, weights, temporal attributes, and dual-dimensional labels of "topic + keyword"; Step S6, outputting multi-dimensional visualization results and hotspot prediction results, iteratively adjusting model parameters based on a feedback optimization mechanism, and finally generating a structured report and research decision-making suggestions. This application has the effect of improving the quality of mining research hotspots in universities.

The application discloses an interactive self-service analysis and retrieval system based on a large model, which comprises a data source management module, a retrieval library construction module, an algorithm development module, a task scheduling management module and a service publishing module; the data source management module pre-processes initial information data to obtain structured information data; the retrieval library construction module constructs a retrieval library according to the structured information data; the algorithm development module extracts data features in the structured information data and recommends an adaptive retrieval algorithm according to the data features; the task scheduling management module performs retrieval analysis in the retrieval library according to the adaptive retrieval algorithm and an input retrieval instruction; and the service publishing module is used for converting the retrieval analysis result into an API service that can be called. The application aims to solve the pain points of the existing data analysis technology, such as fragmented functions, high operation threshold and lack of special text processing modules. The application provides an efficient solution for data modeling, text mining and service deployment.

The present application provides a kind of student individualized skill formation evaluation method and system based on text mining, the method includes step 1: obtaining the original text of formative evaluation homework, and it is preprocessed, obtain the homework text after preprocessing;Step 2: the homework text after preprocessing is classified, and the homework category to which homework text belongs is obtained;Step 3: for the homework category to which homework text belongs, analysis engine follows corresponding analysis rule set to analyze homework text;Step 4: output evaluation report.The present application can formative evaluation on student's experimental report, project homework and other formative evaluation homework such as this kind of unstructured, involves professional field knowledge, comprehensive and engineering text homework, to identify the professional skill defects of student in technical thinking, problem solving and professional expression ability and other aspects embodied in formative evaluation homework.

This application provides a method and system for classifying consultation questions based on AI text mining. First, a collection of consultation question texts in the target domain is obtained, containing multiple interactive conversation text fragments with temporal attributes. Next, semantic feature extraction processing is performed on the consultation question text collection to generate contextual semantic representations and intent association features for each interactive conversation text fragment. Subsequently, a multi-level attention mechanism is used to process the dynamic classification weight distribution of the generated interactive conversation text fragments, and a target classification label index is determined through semantic alignment processing. Finally, the classification label priority of the classification label library is updated according to the target classification label index, and the updated classification label library is mapped to the consultation question recommendation interface. This achieves intelligent classification and personalized recommendation of consultation questions, improving the efficiency and accuracy of consultation question classification and optimizing the consultation service experience.

This invention discloses a method, system, and storage medium for constructing and optimizing neural networks based on minimizing structural entropy, belonging to the fields of deep learning and structural information theory. The method first constructs the input unstructured data into a graph structure using a preset strategy; then, it constructs an encoding tree for the graph and calculates the structural entropy of the graph under the encoding tree; next, it calculates the k-dimensional structural entropy, the structural information of a given vertex, and the structural information of a given set of vertices by restricting the encoding tree type; finally, with minimizing structural entropy as the optimization objective, it jointly optimizes and trains the neural network with the task objective, and decodes the substantial structure of the data from the optimal encoding tree. This method can enhance the neural network's ability to perceive the data topology, improve model interpretability, and is applicable to data processing tasks in multiple fields such as image classification, text mining, and time series analysis.

The application belongs to the cross field of artificial intelligence and text mining, and specifically relates to a long text intelligent processing method and device based on field self-adaption, which comprises the following steps: inputting a long text to be processed into a pre-trained field classification model, identifying and outputting a field category to which the long text belongs; based on the field category, dynamically loading and generating a target Prompt adapted to the field category from a pre-constructed hierarchical Prompt template library; inputting the long text to be processed and the target Prompt into a dynamic segmentation engine, adaptively segmenting the long text based on semantic coherence by the dynamic segmentation engine, and outputting a plurality of semantically coherent paragraphs and corresponding paragraph semantic vectors; based on the paragraph semantic vectors, combining multi-dimensional importance evaluation indexes, extracting key information from the plurality of paragraphs, and generating an abstract for the long text. The generated abstract contains key information and maintains semantic coherence.

The present application belongs to the technical field of chemical accident safety analysis, and discloses a chemical accident cause analysis method based on text mining and cause inference, which comprises: directional semantic segment extraction and cleaning treatment on the obtained chemical accident text; training of a pre-constructed deep learning model by using historical chemical accident data, cause entity recognition on the optimized cause description data; automatic aggregation of chemical cause semantic vectors into a cause theme set by using a density clustering algorithm; cause theme effect strength calculation on the cause theme set according to a pre-set multi-dimensional strength calculation index set, construction of a partial ancestor graph, and modification processing on the partial ancestor graph; cause and effect strength inference on the chemical cause and effect structure based on a structural equation model, counterfactual intervention simulation on the quantitative cause and effect network, and determination of the optimal prevention strategy. The present application can realize automatic processing of unstructured text and improve the automation and objectivity of cause analysis.