With the development of e-commerce, the three-dimensional bin packing problem is crucial for improving logistics efficiency and reducing costs. In this study, an improved deep neural network algorithm (IDNNA) is proposed. This algorithm utilizes the size information of ordered items to design neural network inputs, enabling a better understanding of irregular items. The neural network outputs the box type with the maximum loading rate, and training efficiency is improved by designing masking information. The algorithm’s performance is optimized through multiple experiments. IDNNA demonstrates excellent performance on public datasets, significantly improving efficiency when handling large-scale instances, successfully reducing the average number of attempts by up to 81.81%. This research provides an efficient and accurate solution for the e-commerce warehousing industry, significantly improving packing efficiency and reducing costs.