Abstract: To solve the problem of action recognition in indoor environment, a method for human falling recognition in indoor environment was proposed. First, a lightweight 3D network, which uses grouping convolution and factorization to lighten the network structure for action classification, was proposed. Then 2D subnetworks and lightweight 3D sub-networks were fused to improve behavior recognition network based on the 3D convolution. Finally, visual saliency detection was performed on video frames to improve the accuracy of real scene recognition by enhancing the correlation between background texture and human behavior. Results show that the network's parameter is reduced to 6.9×10⁶ and the floating point of operations is reduced to 6.9×10⁹. The algorithm achieves 81.5% accuracy in the task of indoor fall behavior recognition.