Aiming at the problems of privacy protection, fall detection and low recognition rate in home behavior recognition of the elderly, a new human behavior recognition algorithm based on WiFi signal is proposed in this paper. Firstly, 10 kinds of daily life behaviors of the elderly (drinking water, falling, sitting down, etc.) were collected in the simulated home environment; Then, the extracted WiFi channel state information is denoised by Butterworth filter, and the dimension is reduced by principal component analysis; Finally, the processed CSI signals with clear features are input into the attention based bidirectional long short-term memory model for behavior classification. The efficient bi-directional structure and attention mechanism not only produce more informative features, but also improve the generalization performance of behavior recognition; Experimental results show that, compared with some benchmark methods, the proposed algorithm can achieve the best recognition performance for all activities on both public data sets and self-collected data sets, and the accuracy rates are 98% and 96% respectively.