To improve the safety of electrical equipment and ensure the stable operation of the power system, it was necessary to effectively detect and identify partial discharges in high-voltage cables. This study develops a high-voltage cable partial discharge detection and recognition system based on the heterogeneous sensor data fusion. In this system, the distribution of cable electric fields were detected by using the electric field sensors, and identified insulation hazards in cable joints, the occurrence and degree of partial discharge in hazard areas were detected by using the pressure wave sensors, the improved adaptive threshold discrete wavelet transform was employed for signal denoising, the data classification features was enhanced by using the improved Gram angle field feature transform to, and the partial discharge pattern recognition was realized by using the residual convolutional neural network with improvement of the efficient channel attention. The sharp discharge, internal discharge, and surface discharge were selected as the objective to conduct experimental test, the results shows that the system can accurately detect the partial discharge characteristics of cables and effectively identify the discharge mode of high-voltage cable defects. The partial discharge detection rate in the laboratory reached 100%, and the discharge mode recognition rate reached 96.0%. It also performed well in engineering application environments, which is of great significance for the safety of cable use and the stability of power grid operation.