objectives Radiographic examination is currently the most commonly used method for diagnosing developmental dysplasia of the hip (DDH). In recent years, artificial intelligence (AI) has made significant advances in image recognition, segmentation, decision-making, and statistical analysis of a large number of data sets. Our study aim is whether AI model can be accurately measured angles in pelvic radiography of hip. Then evaluation of AI model effectiveness of pelvic radiographs in diagnosis of DDH and BDDH. Methods A total of 1029 patients, comprising 273 men and 757 women (aged 18–84 years, median age: 33 years) who underwent pelvic radiography examination between January 2020 and January 2022 were retrospectively included in this study. The images were randomly allocated into the training set (720 cases), validation set (103 cases), and test set (206 cases). The anatomical key points were marked: L-fhc, L-uar, L-tar, L-lt, R-fhc, R-uar, R-tar, and R-lt. The Sharp, Tönnis, and Center edge (CE) angles were calculated automatically based on the above coordinates and corresponding rules. Hip development situation was compared among measurements obtained by the AI model and those obtained manually by two radiologists. The area under the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic effectiveness of the AI model. Results Manually- and AI model-measured results showed no significant differences in terms of Sharp, Tönnis and Center edge (CE) angles (all P > 0.05). ICCs and correlation coefficient r values were greater than 0.75, indicating that AI model and manual measurements had good repeatability and were positively correlated. AI model measurement results are highly consistent with manual measurement results, with smaller errors. Both AI model and manual measurement results had similar repeatability. The AI model measurement was therefore faster than the radiologists (P < 0.001). AI model measurement had a high diagnostic accuracy, sensitivity and specificity of DDH. AI model has high diagnostic performance for DDH. AI model and manual measurements were basically consistent with clinical diagnosis results (P < 0.05). AI model can be used to evaluate the hip condition by measuring hip sharp, Tönnis and CE angles, which are similar to the clinical diagnosis results and can be used for the auxiliary diagnosis of DDH and BDDH. Conclusion AI model measurement results are highly consistent with manual measurement results. The AI model measurement was far faster than the radiologists. Sharp, Center edge, and Tönnis angles measured using the deep learning based convolutional neural network model can be used to diagnose DDH and BDDH with a high diagnostic performance. AI model can completely replace manual measurement key angles of hip and diagnosing DDH and BDDH, faster and more precise.