Abstract: The shock resistance capability of ship power equipment is an important guarantee for the safe and stable operation of ships, and a reasonable evaluation method can be more convenient for obtaining the prediction results of power equipment shock resistance. First, the impact processes are divided into the low/high speed collision impact process and non-contact explosion impact process, and the non-integrated and integrated shock resistance methods of ship equipment are described in terms of the master-slave system. The research progress is then summarized and key research technologies in the field of shock resistance for ship power equipment are put forward. The non-integrated methods of ship equipment are divided into three categories, namely the equivalent static method, dynamic design analysis method and time-domain analysis method, and combined with the characteristics of integrated methods. It is concluded that the equivalent static method is only applicable to structurally simple power equipment with low frequency effects; the dynamic design analysis method neglects the effects of adjacent equipment, vibration isolation system and hull structure on impact input loads; and the time-domain analysis method is only applicable to specific scenarios or shock signal inputs such as explosive shocks. The integration of ship equipment takes into account the multi-degree-of-freedom system formed by the hull structure–base–vibration isolation system–equipment, and the calculation results are closer to the actual working condition data, but a large amount of resources is consumed in the modelling and calculation process. Finally, the author forecasts and summarizes important development directions for ship power equipment shock resistance, including the accurate selection of evaluation methods, reasonable improvement of shock resistance test rigs, extraction and analysis of impact response signals, analysis of the multi-field coupling effects of power equipment, simplification study of the physical models of power equipment, research on the equivalent substitution of internal flow media, and the combination of data mining and deep learning methods.