Research on real-time analysis technology of urban land use based on support vector machine

Abstract One of the main problems that traditional support vector machine (SVM) has to solve is how to dynamically determine the kernel parameters and penalty parameters of the kernel function in time, along with the increasing amount of data and the changing data structure and characteristics. A new method is proposed for dynamic acquisition of SVM parameters by fruit fly optimization algorithm (FOA) based on the analysis of the classification and aggregation of land use data in urban industry. FOA-SVM aims at the relationship between feature words in the classification process and the core words of different activity semantics in context. In an incomplete date set of initial feature words, FOA-SVM can extract new feature words from the semantic association of feature words to improve the feature word date set. The dynamic parameters of SVM can be obtained through continuous training with FOA, and the accuracy of classification can be improved. The experimental results showed that FOA-SVM can process multi-feature synchronous classification according to different activity semantics and efficiently control the operation of the whole classification process, so as to obtain higher classification accuracy and stronger robustness in multi-source web date categorization. The efficiency of land use real-time analysis is improved.