Objective: The study aims to classify the foot types of young Chinese males, extract characteristic indicators of foot types, and construct a standard foot type database. Methods: Foot type data from 1483 healthy young male individuals were collected. Spectral clustering algorithm was utilized to categorize foot types, and a Deep Neural Network (DNN) was employed for the training of the classification model. Layer-wise Relevance Propagation (LRP) and the correlation coefficient method were combined to extract foot type features, comparing the differences in various foot type characteristics. Results: Spectral clustering yielded 4 distinct foot type categories. Foot Type 1 is characterized by a high-arched foot with a prominent big toe, inwardly rotated fifth toe, and a high heel with a wide foot. Foot Type 2 is characterized by a narrow foot with hallux valgus. Foot Type 3 is characterized by a low-arched foot with hallux valgus. Foot Type 4 is characterized by a high-arched foot with a prominent big toe. By integrating interpretable neural networks and the correlation coefficient method, 9 indicators were extracted from 27 foot type indicators, including the heel to the sole length, hallux height, navicular bone height, lateral malleolus height, hallux valgus angle, fifth toe angle, dorsal foot circumference, heel angle, and longitudinal arch angle. The classification model constructed with these extracted indicators achieved an overall discrimination accuracy rate of 93.67%, higher than the 86.91% achieved by linear discriminant analysis. Conclusion: Chinese men can be classified into four typical foot types. By examining the rearfoot, midfoot, and forefoot regions, nine key foot morphology parameters, including length, height, circumference, and angle, can be extracted. These parameters provide both theoretical and empirical support for establishing normative data on foot morphology for young Chinese men and for advancing biomechanical research on the foot and ankle.