The early detection of diabetes is of great significance for successful control of diabetes, prevention of complications, and reduction of prevalence. Existing diabetes diagnosis models based on machine learning have weak precision due to insufficient generalization ability. Therefore, this study proposes a multi-layer perceptron model combined with batch normalization to ensure the consistency of data distribution in the model. The proposed model is based on the PIMA training set for training evaluation. The experimental results show that the model has sound generalization ability in early recognition of diabetes, fast convergence, and high accuracy.