Abstract: Aiming at the privacy exposure, high technical complexity, and low recognition accuracy existing in the current human motion recognition technology, this paper proposed a human motion recognition method based on a pyroelectric infrared (PIR) sensor. Firstly, a set of PIR sensors placed on the ceiling and modulated by the field of view were used to collect the infrared heat radiation signal emitted by the human body when moving, and the voltage analog signal output by the sensor was filtered and amplified, and then transmitted to the PC through the ZigBee wireless module and packaged into raw data. Secondly, the two-way sensor output data of the original data feature was fused, and the fused data was standardized and packaged into training dataset and test dataset. Then, a two-layer cascaded hybrid deep learning network was proposed to be a classification algorithm of human motion based on the characteristics of the data. The first layer used one-dimensional convolutional neural network (1DCNN) to extract features from the data, and the second layer used gated recurrent unit (GRU) to save historical input information to prevent loss of valid features. Finally, the training dataset was used to train the network model to obtain a classification model with the best parameters, and the correctness of the model was verified through the test dataset. The experimental results show that the accuracy of the proposed motion recognition technology model for basic motion classification is higher than 98%. Compared with image motion recognition or wearable device motion recognition, it realizes high-precision human motion recognition with real-time, convenience, low cost and strong confidentiality.