Smart community construction is an integral part of smart city construction, and smart community management requires huge amounts of data as support. Currently, the data generated by some smart communities is scattered, and this data needs further analysis to realize value. This paper primarily studies data classification and parameter optimization. First, a novel K‐means clustering group support vector machines (SVM) method is proposed for data classification. For the parameter optimization problem of SVMs, evolutionary computation is used to seek the optimal solution through iterative evolution in a population composed of some feasible solutions. Then, the improved gray wolf optimization (iGWO) algorithm is used to optimize parameters and select features of SVM. Finally, to alleviate the situation that the minority samples are easily misjudged as noise samples due to the redundant features in the initial data, an oversampling method based on the iGWO and synthetic minority oversampling technique (SMOTE) is proposed, called iGWO–SMOTE–SVM. The experimental results demonstrate that the suggested approach on the six UCI datasets has acceptable accuracy, F1, and G‐Mean, which can well serve the construction of smart communities.

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