In order to accurately evaluate the operational status of secondary equipment in an intelligent substation, this paper establishes an evaluation index system of the secondary equipment status. Combined with the differences of various machine learning algorithms, it also proposes a secondary equipment condition evaluation method based on multi-model ensemble learning. The method adopts a double-layer structure. In the upper layer, k-fold verification is carried out by dividing the data into several base learners. In the lower layer, a fully connected cascaded neural network is used to fuse multiple base models, and the improved Levenberg Marquardt algorithm is used to train the neural network to accelerate model convergence. The case analysis shows that the proposed method can accurately evaluate the operational status of the secondary equipment, and provide guidance for the maintenance of the intelligent substation system and secondary equipment. This work is supported by the Science and Technology Project of State Grid Corporation of China (No. 521200190081).