In order to solve the problem of incomplete representation when the wearable MEMS sensor is used to detect human fall behavior in multiple scenes, a SVM human fall detection and recognition method is proposed based on improved sparrow search algorithm (ISSA). Firstly, the wearable MEMS sensor is used to collect the discrete attitude data of human body. Then, the acceleration threshold and angular velocity threshold eigenvectors are found through the time sliding window and the first-order judgment was performed. At the same time, an ISSA-SVM detection model of fall state is constructed, that is, the kernel parameters and penalty factors of SVM are adaptive optimized by the improved sparrow search algorithm to obtain the optimal classification model. Finally, according to the SVM classification model, the data of the first-level decision are analyzed to judge whether the fall is real. Experimental simulation and product application results show that the test accuracy of the proposed ISSA-SVM model for the detection of human accidental falls in different scenarios is more than 98%, and the failure rate is reduced. After many tests, the fall detector shows good robustness.