This work presents a method of artificial neural network (ANN) to predict the stretch formability of ZK60 alloy associated with crystal deflection angle, tensile mechanical properties. To discuss the regulation of texture, the balance of strength and plasticity, an ANN model was constructed with datasets of texture and mechanical properties parameters that were prepared from published literature. The results of model training and prediction based on 7 predictors show that the model has good generalization ability and the prediction accuracy is over than 0.93. The quantitative evolution of microstructure was analyzed and calculated. Correlation analysis of the predicted results showed that the tensile mechanical properties and the texture distribution play a nonlinear role in regulating formability. Experimental results validate the reliability of the ANN model. Furthermore, high formability conditions were analyzed and discussed.