Background: This study aimed to differentiate whether diabetic nephropathy (DN) is complicated by minimal change disease (MCD) through the differences in podocyte foot process morphology, and subsequently establish an Artificial Intelligence-Assisted (AI-assisted) Diagnostic Model through machine learning of renal tissue electron microscopy images. Methods: Patients diagnosed with DN with nephrotic syndrome and treated in our hospital from January 2014 to December 2023 were selected. Patients were divided into the DN group and the DN with MCD group (DN+MCD group). Podocyte morphology's diagnostic value was assessed by measuring foot process width and quantifying slit diaphragm changes via Nephrin immunohistochemical staining. This study pioneers developing a machine learning-powered diagnostic model based on renal electron microscopy imaging to differentiate DN cases with or without concurrent MCD. Results: In 51 patients, DN+MCD patients exhibited wider podocyte foot processes and reduced Nephrin expression compared to DN. A total of 622 electron microscopy images were used for model establishment and internal validation, while 225 electron microscopy images were used for external validation. A model based on Mobilenetv2 was successfully established, achieving a maximum accuracy of 93.3% in differentiating whether DN is complicated by MCD using a single image. When at least 11 random images were input, stable reports were obtained with an accuracy of 98%. External validation showed that the model had good sensitivity and specificity in differentiating whether DN is complicated by MCD (100%, 83.33%). Conclusion: Podocyte foot process morphology has diagnostic value in differentiating whether DN is complicated by MCD. Our AI model addresses the unmet clinical need for reliable differentiation between DN with and without concurrent MCD. Additionally, it establishes a foundational framework for AI-powered analysis of renal imaging data to improve disease diagnosis and prognosis prediction.