Due to the harsh working environment and complex working conditions, the hydraulic pump is often in the working state of variable load, severely challenging its condition monitoring and fault diagnosis. However, the existing model-based and data-driven methods have some limitations in fault diagnosis, so a fault diagnosis method based on the fusion of mechanism model and data-driven is proposed. First, the virtual prototype model of the hydraulic pump is built, and the faults under different loads are simulated to obtain the simulation pressure signal. Then, the hydraulic pump is tested for fault, and the experimental pressure signals of load and fault state corresponding to the simulation signal are collected. Following that, the variance of simulation and experimental data is calculated according to the proposed variance weight fusion method, and the optimal weight calculated by variance is used to fuse the simulation and experimental data. Finally, the fusion data is input into the deep convolutional neural networks with wide first-layer kernels for fault diagnosis under single and mixed loads. Experimental results show that this method can significantly improve the accuracy of diagnosis, and the accuracy is 2.42% and 12.92% higher than that of single model-driven and data-driven diagnosis methods in the case of mixed load, which verifies the effectiveness and superiority of this method.