6 results about "Search engine technology" patented technology

The invention belongs to the technical field of search engines, and particularly relates to an optimized search engine construction method and system based on feature deep learning, and the system comprises a search operation unit, a monitoring and early warning unit and a management and control terminal, the search operation unit comprises a multi-dimensional semantic feature mining module, a user implicit intention analysis module, a deep feature fusion modeling module and a dynamic retrieval strategy generation module; a multi-dimensional semantic feature set and a user intention feature set are formed through a multi-dimensional semantic feature mining module and a user implicit intention analysis module, and a deep feature fusion modeling module performs deep fusion of data features and user intention features through an attention mechanism and cross validation optimization. The dynamic retrieval strategy generation module is used for providing high-quality feature support for retrieval strategy generation, the accuracy of retrieval results is guaranteed, the dynamic retrieval strategy generation module generates dynamic strategies adaptive to different requirements and data states on the basis of reinforcement learning, and the retrieval accuracy and retrieval efficiency of a search engine are remarkably improved.

The present invention discloses a search engine system based on text sentiment analysis, which relates to the field of search engine technology and includes a real-time parameter capture and storage module, an anomaly analysis and model comparison module, a risk assessment module, and a countermeasure module: In the real-time parameter capture and storage module, during the sentiment analysis process, each text will generate a series of parameters, and the parameters generated when the sentiment analysis model performs text analysis are captured and stored in real time to ensure low latency and integrity of data flow. The present invention enables the system to accurately capture complex emotions and avoid misjudgment by introducing the sentiment polarization index and the expectation violation index. Real-time parameter capture ensures low latency, and multi-level analysis of machine learning improves robustness. Through the classification of low, medium, and high risk levels, the system implements on-demand intervention and resource optimization to avoid business losses and damage to brand image, and ensure that enterprises can efficiently respond to market feedback and uncertainty.

The application belongs to the technical field of search engines, and discloses a search engine optimization method, device, equipment and storage medium, the method obtains a preset query statement and historical non-displayed content of a search engine; according to a preset language model after training, feature extraction is performed on the historical non-displayed content and the preset query statement to obtain the correlation between the historical non-displayed content and the preset query statement; a query statement candidate set is generated according to the query statement corresponding to the historical non-displayed content; the query statement candidate set is filtered according to the correlation to obtain a target query statement, and the search engine is optimized according to the target query statement. In the application, the target query statement is generated for the historical non-displayed content in the search engine, the score of the historical non-displayed content in the search system retrieval recall stage is improved, the recall probability of the content can be improved, the retrieval result is more accurate, and the search engine is optimized.

The present disclosure provides a page search method, device, equipment and medium, relates to the field of computers, in particular to computer network technology, search engine technology and software application technology. The method comprises the following steps: determining a candidate page based on a query request; determining at least one candidate page area in the candidate page; determining the weight of each of the at least one candidate page area based on a preset rule for the candidate page; calculating the matching degree between the query request and each of the at least one candidate page area; and determining the matching degree between the query request and the candidate page based on at least the matching degree between the query request and each of the at least one candidate page area and the weight of each of the at least one candidate page area.

The invention relates to a search keyword trimming method and device, electronic equipment and a computer storage medium, and belongs to the technical field of search engines. The search keyword trimming method comprises the steps that a search statement for a target library is obtained, the search statement is preprocessed, and keywords meeting preset standard conditions are obtained; calculating importance scores of the keywords in stored documents of a target library and semantic similarities among the keywords; the key words are trimmed based on the importance scores and the semantic similarities, target key words are obtained, the importance scores of the target key words are larger than a preset score threshold value, and the semantic similarities between the target key words are smaller than a preset similarity threshold value. According to the method, the keyword can be accurately determined, so that the accuracy of a search result is improved.

The invention relates to the technical field of generative search engines, and discloses a generative search engine-oriented content acquisition probability evaluation and structure optimization system. The system comprises a multi-dimensional probability quantitative evaluation module which decomposes answer content into a fact statement unit, calculates source credibility, semantic association degree and multi-source consistency three-dimensional sub-scores in parallel, fuses a generation unit credibility acquisition probability, aggregates an output unit credibility acquisition probability, and identifies a low-confidence evaluation factor; the credibility-driven structured optimization module is used for carrying out semantic segmentation on answer contents according to the unit credit collection probability, carrying out enhanced presentation on high-confidence facts and carrying out risk prompt and evidence association display on low-confidence statements; and the closed-loop feedback optimization module is used for triggering supplementary retrieval according to the evaluation factors, inputting new evidences into re-evaluation and driving the structured optimization module to perform real-time adjustment. According to the method, credibility quantification, structured presentation and retrieval-optimization closed loop are realized, and the credibility and interpretability of the generated answer are improved.