Partial discharge is an important indicator for assessing the insulation condition of power equipment, and accurate identification of partial discharge types is essential for ensuring the safe operation of both power equipment and power grid. However, due to weak partial discharge signals and the similar characteristics of difference types of partial discharges, existing partial discharge monitoring methods based on single data source suffer from low information utilization and limited identification accuracy. To address these challenges, a refined identification method for partial discharge in power equipment based on acoustic-optical fusion imaging feature analysis is proposed. First, sliding feature extraction is performed on the collected discharge audio and acoustic images to form a feature matrix of acoustic-optical fusion. The feature matrix is then embedded into a multivariate time series, and a gate controlled dual-axis encoding model is used to extract information, allocate weights, and recognize features in parallel along both the time and feature dimensions. Finally, the probability of the recognized feature vector belonging to each discharge category is calculated to achieve high-precision identification. Results show that the proposed method can achieve accurate identification of multiple types of discharge with an accuracy of up to 98.32%, outperforming identification methods based on single-source features.