Journal of Electronic Systems Xiliang Nie, Hanxing Li 14 4 2024 https://doi.org/10.6025/jes/2024/14/4/132-138 https://www.dline.info/jes/fulltext/v14n4/jesv14n4_3.pdf With the rapid development of computer technology and big data, algorithms such as deep learning and neural networks have been widely applied. Among them, deep learning has gradually become a hot topic and mainstream direction in intelligence. This computer model constructed using multiple processing layers has shown significant effects in aerospace, electrical engineering, automotive, and urban transportation industries. This paper uses genetic algorithms and particle swarm optimization algorithms in deep learning to study and optimize the layout of ship engine room electrical equipment. Firstly, we analyze the application of genetic algorithms to solve the layout planning of ship engine room electrical equipment. The arrangement of equipment and the utilization of ship’s electrical wires are recombined through data analysis to understand the correlation of various factors affecting the layout fully. Secondly, we use particle swarm quantum algorithm to optimize the layout structure of genetic algorithms, establishing constraints to obtain the final objective function path. Finally, attention should be paid to equipment anomalies before the layout of ship engine room electrical equipment. Deep learning establishes a spectrogram anomaly detection database to explore the location of abnormal states through detection algorithms. The research results show that the optimization of ship engine room electrical equipment layout based on deep learning using particle swarm algorithms has achieved good results in terms of usage.