A human action recognition algorithm is proposed based on the decision-making level fusion with spatial and temporal features. Shape context feature of human body is extracted to match the contours of template images and test images in the spatial domain, while the motion feature is described by a changing spatial feature sequence in the time domain. Then, the motion feature is combined with the robust spatial feature for effective human action recognition. At the recognition stage, the dynamic time warping is applied to calculate the posterior probabilities of two kinds of features for each class. The weighted-average method is used to fuse the two posterior probabilities at the decision-making level, and the corresponding class with the maximum probability is recorded as the final classification result. Aiming at the dynamic time warping algorithm, we propose an improved searching strategy based on the elliptic boundary constraint, which can effectively reduce the space for searching for the optimal path, while eliminate the noise interference in the video sequence. The constraint performance of elliptical boundary is analyzed from two aspects of computational complexity and recognition accuracy. Experimental results show that the performance of elliptical boundary constraint is better than that of the parallelogram and diamond boundary constraints, and the optimal boundary size range is given. Experimental results on Weizmann, KTH and UCF101 datasets demonstrate that the average recognition rate of the proposed method is higher than 93.2%, 92.7% and 81.2%, respectively, indicating that the proposed method can effectively obtain the efficiency and stability of indoor intelligent monitoring system.