High dimensional multi-attribute big data association mining method based on semantic feature fusion

High dimensional multi-attribute big data mining is a key technology for processing natural language and database access. A high-dimensional multi-attribute big data association mining based on semantic feature fusion is proposed. Data collection and cleaning techniques are used to achieve bias learning in the natural language processing process. Semantic feature fusion methods are used to remove redundant data, fill in gaps, and smooth data. A residual feature decomposition model for high-dimensional multi data big data is constructed. Multiple semantic segmentation methods are used to extract multi-source semantic feature quantities such as interests and emotions from big data. Time sequence analysis and correlation analysis methods are used to filter and clean redundant data in the big data mining process. Standardized semantic feature fusion methods are combined to cluster the high-dimensional multi data big data mined. And segmentation, we realize spatiotemporal and causal correlations for high-dimensional, multi data, and big data. The simulation test results show that this method can effectively achieve spatiotemporal correlation and causal correlation in big data association mining, realize semantic sentiment analysis of big data, accurately evaluate the diversity of information dissemination across communities, and provide an intuitive choice for classification problems with similar data features.