In the application environment of human activity recognition， it is still challenging to extract sufficiently reliable features from the original sensor data. The hybrid signal decomposition technology of discrete wavelet transform （DWT） and variational mode decomposition （VMD） is used to extract the salient feature vectors from the original sensor signals to identify various human activities. Using a variety of machine learning classification algorithms， such as K-nearest neighbor， random forest， LightGBM and XGBoost， the effectiveness of the proposed algorithm is tested on UCI-HAR and SCUT-NAA data sets. Experimental results show that by using the hybrid signal decomposition technology， the recognition accuracy of all classification algorithms has been improved， with the maximum classification accuracy of 98.91% for UCI-HAR dataset， which has improved by 1.79% compared to not joining the decomposition algorithm. The maximum classification accuracy of SCUT-NAA dataset reaches 95.52%， which has improved by 3.2%. In human activity recognition， through the use of DWT-VMD hybrid signal decomposition technique， more effective features can be extracted from the original signal and the recognition accuracy can be further improved， showing the certain practical value of the technique.