The inspection of protection platens in substations at present still relies on manual operation, which is time-consuming and error-prone. It also restricts the development of more intelligent secondary equipment in substations. Given this, a novel status recognition method for a substation protection platen based on multi-strategy segmentation and fusion and morphological feature identification is proposed. With a cabinet image collected by the mobile terminal, the platen region is first dealt with perspective transformation to eliminate the distortion caused by the image acquisition angle. Then, a multi-strategy segmentation and fusion method is proposed to extract the valid platen region, within which multi-threshold segmentation is applied in the HSV space while K-means clustering is adopted for segmentation in Lab space. Thus, the valid platen region is obtained by fusing the two kinds of segmentation results. Subsequently, morphological features of the valid platens including direction angle and aspect ratio are calculated, based on which the state corresponding to different features is distinguished. Then the two state results are merged into the final operating state of platens. Results in application for different cases for cabinet images with complex background show that the proposed method achieves good accuracy and applicability. This work is supported by the National Natural Science Foundation of China (No. 51741907).