The traditional machine learning methods for wearable sensorbased human activity recognition tasks usually require manual feature extraction, and the deep neural network that can automatically extract human activity data features is becoming a new research focus. At present, DeepConvLSTM, which is combined of convolutional neural network (CNN) and long shortterm memory (LSTM) recurrent neural network, has better recognition accuracy than other recognition methods. To solve the difficulty of training neural networks with long shortterm memory recurrent unit, the paper proposes a fusion model based on convolutional neural network and gated recurrent unit (GRU), and the performance on three public data sets (ACT data set, UCI data set and OPPORTUNITY data set) is compared with convolutional neural network and DeepConvLSTM. The experimental results show that the recognition accuracy of the model on three public data sets is higher than that of convolutional neural network and is close to DeepConvLSTM, but the convergence speed of the model is faster than that of DeepConvLSTM.