Pre maintenance of military power stations based on health status assessment is of great significance in improving their reliability and safety. Therefore, a CNN-MD based evaluation method is proposed to evaluate the health status of military power stations. It establishes a CNN military power station health status recognition model, which can recognize and output health status categories by training the model with different state samples. Introducing the MD algorithm again, calculating the MD distance between samples and healthy samples in different states, and normalizing it into a health index can further quantify the health evaluation results of power stations. Using a certain military power stations to artificially simulate different health conditions, experimental data was obtained. The model was able to effectively distinguish between normal, degraded, and attention states, and a quantitative evaluation health index of 0.6 was obtained as the critical threshold for normal states, and below 0.6 was the specific quantitative evaluation value for degraded states. The effectiveness of the proposed method was verified. This method combines CNN and MD to achieve the qualitative and quantitative evaluation of the health status of military power stations, providing a basis for pre maintenance.