When identifying activities, the traditional recurrent neural network (RNN) recognition method does not consider the problem of strong dependency among sensor activity data, resulting in a decrease in recognition accuracy. In order to improve the recognition accuracy and solve the problem of strong dependency among sensor activity data, the long-short term memory network (LSTM) is used for activity recognition. The LSTM considers both the input of current points and the output of the previous points to maintain strong dependency among data. However, it has low time efficiency in feature extraction for sensor activity data. The convolutional neural network (CNN) can share convolution kernels and extract obvious feature vectors from disordered data. We present a CNN-LSTM activity recognition (CLAR) method. The CLAR uses the CNN to extract the feature vectors in the activity sequence data, takes the extracted feature vectors as the input of the LSTM, and uses the interaction between thresholds to recognize activities, which makes the highly dependency among activity data become an advantage of activity recognition, thus improving the accuracy and time efficiency of activity recognition. Experimental results show that the CLAR method is 9% more accurate than the single neural network activity recognition method, and it consumes 10% less time on average.