Hydraulic mechanical is essential components of high-voltage circuit breakers in power systems, used to control and protect equipment from the destructive effects of abnormal currents. Accurately and efficiently detecting operational faults in hydraulic mechanical is of significant practical importance for improving the overall performance of high-voltage circuit breakers and advancing ultra-high-voltage power grids. To predict the fault location of circuit breaker hydraulic mechanical, this paper proposes a fault diagnosis method based on the degradation characteristics of hydraulic oil and hydraulic mechanical faults through multi-source heterogeneous information fusion. Specifically, a hydraulic oil detection system is first designed based on three physical and chemical indicators. Next, a mapping model is constructed between hydraulic oil condition data and hydraulic system faults, establishing a data-driven CNN model for fault diagnosis of the hydraulic system. Finally, fault diagnosis experiments on the hydraulic mechanical are conducted using the CNN model, and the effectiveness of the detection is verified.