The study developed and validated a predictive model to identify patients with psoriasis who will not respond to IL-17A inhibitors during initial treatment. The research included 370 patients in the training set, 247 in the internal validation, and 511 in the external validation from 26 Chinese hospitals. Primary non-response was defined as failure to achieve at least 75% improvement according to the PASI (Psoriasis Area and Severity Index) by week 12 of treatment. Key predictors of non-response were higher body mass index (BMI), absence of family history of psoriasis, prior biologic therapy, and higher initial PASI score. Failure to achieve a 40% PASI improvement at week 4 was shown to be a strong predictor of future non-response. The developed model achieved a strong discriminative ability with an AUC value of 0.766 in the training group and maintained stable performance in both internal validation (AUC 0.706) and external validation (AUC 0.708). Based on the model, an online calculator for individualized risk estimation was created, which can help doctors in the early identification of patients with a high risk of non-response and contribute to more accurate decision-making in the treatment of psoriasis.