Multilayer networks, as an important branch of network science, have become a powerful tool for revealing and analyzing the internal structures of complex systems. Within these networks, community detection is particularly crucial, as it assists in uncovering hidden patterns within the network. We construct a seed node selection method based on the local structural characteristics of network nodes and, by integrating deep learning methods, establish a local information expansion strategy. This approach effectively identifies and expands community boundaries, developing a novel multilayer network community detection algorithm—the Layered Information Expansion Detection Algorithm (LIEDA). Its exceptional performance has been experimentally verified using multiple real-world datasets. Compared with existing technologies, the LIEDA has considerable accuracy, stability, and adaptability advantages. Compared with various popular benchmark algorithms, the model has substantially improved multiple evaluation metrics across several authoritative public and synthetic datasets.