Human activity recognition (HAR) has a wide range of applications in medical, military, smart home, and other fields. Feature extraction using traditional machine learning methods is challenging and imprecise. Aiming to address the aforementioned issues and leveraging the timing characteristics of the sensor, we propose a DRSN-GRU model that integrates the CBAM attention mechanism. The problem of gradient explosion and vanishing, which was caused by increasing network depth in traditional sequential models was effectively avoided. The parallel structure allows both branches to be given the same priority. The deep residual shrinkage network (DRSN) was used to extract the deep spatial characteristics of the data, while the gated recurrent unit (GRU) was used to learn the characteristics of the activity samples over time series. The two channels simultaneously extract features of different dimensions from the samples and distribute the weight of these features using the CBAM module. Finally, the end-to-end human activity recognition is achieved through the Softmax layer for classification. The WISDM public data set was used for verification, and the model achieved an average accuracy of 97.6%. This demonstrates a superior recognition effect compared to the traditional machine learning and neural network models proposed by previous researchers.