Aiming at the problems that mechanical equipment signals in actual operation are susceptible to noise interference, making it difficult to accurately extract fault features, and that the information from a single position of the equipment cannot fully reflect operational status, this study proposes an improved spatio-temporal fault classification method of signal adaptive decomposition and multi-source data fusion. Firstly, an improved signal adaptive decomposition algorithm named signal adaptive variational mode decomposition (SAVMD) is proposed, and a weighted kurtosis sparsity index named weighted kurtosis sparsity (WKS) is constructed to filter out intrinsic mode function (IMF) components rich in feature information for signal reconstruction. Secondly, multi-source data from different position sensors are fused, and the data set obtained by periodic sampling is used as the input of the model. Finally, a spatio-temporal fault classification model is built to process multi-source data, which reduces noise interference through an improved sparse self-attention mechanism and effectively processes time step and spatial channel information by using a dual-encoder mechanism. Experiments on three public mechanical equipment fault datasets achieve average accuracy rates of 99.1%, 98.5%, and 99.4% respectively. Compared with other fault classification methods, it has better performance, good adaptability and robustness, and provides a feasible method for fault diagnosis of mechanical equipment.