Abstract: In order to improve the accurate mining ability of heterogeneous multi-core platform big data, a method based on semantic segmentation is proposed. The fuzzy information detection model of heterogeneous multi-core platform big data is constructed, and the fuzzy directional clustering analysis of heterogeneous multi-core platform big data is carried out by using association features extraction method. The output autocorrelation features matching model of heterogeneous multi-core platform big data is constructed, and the features extraction and statistical analysis of heterogeneous multi-core platform big data are carried out by semantic features extraction method. The semantic dynamic features analysis model of heterogeneous multi-core platform big data is established, and the statistical characteristics of heterogeneous multi-core platform big data are extracted. According to the features extraction results of heterogeneous multi-core platform big data, the fuzzy C-means clustering method is used for big data clustering, and the semantic segmentation is used for adaptive optimization in the process of heterogeneous multi-core platform big data mining to realize the optimized mining. The simulation results show that the proposed method has higher accuracy and better features resolution, which can improve the mining and detection ability of heterogeneous multi-core platform big data.

Key words: semantic segmentation, heterogeneous multi-core platform, big data, mining