Using a sample of Chinese junior high school students (N = 1133), this study examined the latent heterogeneity and structural characteristics of mobile phone addiction risk. Latent profile analysis was conducted to identify subgroups with varying levels of risk. Network analysis was then used to model the relationships among smartphone addiction, materialism, and childhood neglect and compare structural differences across subgroups. Finally, five machine learning models were applied to model smartphone addiction scores and compare model performance across different combinations of variables. Results revealed three distinct risk groups of smartphone addiction. Network analysis indicated that loneliness-related nodes exhibited the highest expected influence in the overall network. Across latent profiles, childhood neglect-related nodes consistently occupied central positions, whereas materialism-related nodes showed relatively stable centrality. Network comparison tests further demonstrated significant structural differences across risk groups. In addition, incorporating latent profile information and centrality indices improved model performance, suggesting that these features capture individual differences in smartphone addiction. These findings provide structural evidence for the heterogeneity of mobile phone addiction risk and offer implications for subgroup-specific intervention strategies.