Abstract: To address the limitations of traditional motor control adaptability assessment methods, which rely heavily on manual expertise and lack generalization capability, this study proposes a deep learning-based assessment algorithm to enhance objectivity and intelligence in assessment. Building upon the dynamic characteristics of motor control systems and intrinsic motor properties, a convolutional neural network (CNN) architecture is adopted to establish an evaluation system with control accuracy and response speed as core metrics. Firstly, constructing simulation models for five representative motor types and collecting multi-dimensional datasets comprising operational data and working condition parameters within fixed time windows. Secondly,designing a CNN-based deep learning network to extract spatiotemporal features and temporal correlations, with iterative optimization of key feature weights. Experimental results demonstrate that the proposed algorithm achieves superior performance across multiple evaluation metrics on the test set, while maintaining robust stability under varying load conditions. The findings indicate that this method can effectively quantify the comprehensive adaptability of motor control systems, providing intelligent decision support for motor selection and controller parameter optimization in industrial applications.