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The current research on intelligent diagnosis of ship"s engine room at home and abroad is shallow, and the remote monitoring technology is difficult to meet the efficient demand of ship"s safe operation. Therefore, the research innovatively designs a ship nacelle safety monitoring system. The optimized wolf pack algorithm is used to improve the back-propagation neural network, and the nacelle diesel engine fault diagnosis model is established. Meanwhile, the design of the ship cabin safety monitoring system was carried out by combining virtual reality technology. Experimental validation shows that the average diagnostic accuracy of the fault diagnosis model constructed by the study is as high as 99.102% for five fault types of diesel engines. The safety monitoring system improved the fault identification accuracy by 5.34% compared with other systems, and the response time was 0.048 ms. The results show that improving the weights and thresholds of the back-propagation neural network by using the optimized wolf pack algorithm can effectively improve the diagnostic results of the fault diagnostic model, and the convergence effect is more satisfactory. The cabin safety monitoring system proposed by the study has positive application value in the safe operation of ships and has a certain role in promoting the safe operation of ships.