Integrating Chinese medicine and biomedicine for treating acute ischemic stroke (AIS) presents a promising strategy. Accurately predicting Traditional Chinese Medicine (TCM) heat syndrome types in AIS patients is crucial for guiding appropriate medication use within this combined treatment strategy. In this study, a clinical cohort including TCM syndromes, laboratory markers, and baseline assessments, were collected from 193 AIS patients. We developed a deep learning method with Convolutional Neural Networks (CNNs) to predict heat syndrome types in AIS patients by integrating TCM pattern characteristics and laboratory indicators. Feature importance was assessed using SHapley Additive exPlanations (SHAP) and permutation importance, and partial dependence plots (PDP) were used to explore the relationships between features and predictions. The model with the comprehensive feature dataset achieved an accuracy of 0.95, F1 score of 0.95, and AUC of 0.91 on the test set, exhibiting better performance overall compared to predictions based solely on TCM pattern characteristics or laboratory indicators. Key factors associated with the heat syndrome types included Tongue Teeth Marks, Stool, Sweat, Tongue Fissures, glycated hemoglobin (HbA1c), triglycerides (TG), fasting blood glucose (FBG) and total cholesterol (CHO). In conclusion, this study confirms the effectiveness of the CNN model in predicting heat syndrome types in AIS patients when incorporating TCM patterns with biochemical laboratory indicators.