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At present, the human behavior recognition methods based on millimeter wave radar cannot distinguish similar actions when facing complicated scenes.In addition, there is a low robustness and interference resistance among these methods. To address the above two issues, a generic human behavior recognition method based on millimeter wave radar sparse point clouds is proposed, the method first samples the point cloud using the K-means++ clustering algorithm, and then uses a point cloud activity classification network based on attentional feature fusion for the extraction and recognition of human behavior features, which can take into account both spatial and temporal features of point clouds and has a sensitive perception of the motion of sparse point clouds. In order to verify the effectiveness and robustness of the proposed method, experiments were conducted on the MMActivity dataset and MMGesture dataset, respectively, which achieved 97.50% and 94.10% accuracy on both datasets, outperforming other methods. Furthermore, the effectiveness of the K-means++ point cloud sampling method is further verified, and the accuracy is improved by 0.4 percentage points compared to random sampling.The experimental results show that the proposed method can effectively promote the accuracy of human behavior recognition, and the model possesses a strong generalization ability.