To investigate the effects of different line pepper varieties on the quality of fermented chopped chili, a suitability evaluation model for chopped chili processing was constructed. A total of 45 line pepper varieties were selected and processed into fermented chopped chili. The appearance characteristics, physicochemical indicators and sensory evaluation scores of fresh and fermented pepper samples were measured. Correlation analysis, partial least squares discriminant analysis (PLS-DA), and random forest algorithm were applied to identify key indicators affecting the processing quality of line peppers and to develop a quality prediction model. The results showed significant differences in the quality of fermented chopped chili prepared from different line pepper varieties. Based on correlation analysis and PLS-DA, nine key indicators were selected, and a quality prediction model was constructed using random forest algorithm with coefficient of determinations for the training (R_t²) and prediction (R_p²) sets of 0.9343 and 0.8498, respectively. Variable importance and Shapley Additive exPlanations (SHAP) analysis identified moisture content, capsaicin, L* value, and pectin as the core indicators for determining chopped chili quality, enabling effective quality prediction. This study provides technical support for the efficient selection of line pepper varieties suitable for the processing of fermented chopped chili as well as its systematic quality evaluation.