Each year, a significant number of people succumb to cardiovascular diseases (CVD), making early and accurate prediction of CVD highly clinically significant. Methods based on electronic health records (EHR) and deep learning are increasingly being adopted for this purpose. However, current CVD prediction methods often rely solely on biochemical test data, lacking comprehensive integration of multimodal information, and face challenges in capturing long-range dependencies within complex EHR data. In this paper, we propose a deep learning approach based on the Chinese BERT model and multi-head self-attention mechanism to enhance the accuracy and reliability of CVD prediction. Specifically, we transfer a pre-trained Chinese BERT model to our task, integrating patient chief complaints, demographic data, vital signs such as blood pressure, pulse, and temperature, as well as textual descriptions of preliminary diagnoses by physicians. Features are extracted using the RoBERTa-wwm-ext-large model, and key features and long-range dependencies within the data are captured using a multi-head self-attention mechanism. We evaluated our model on three real-world clinical datasets comprising data from 12,180 patients. Experimental results demonstrate that our proposed method performs excellently across different datasets and text lengths, achieving an AUC of 0.98 and an accuracy of 92.93%. Additionally, comparative results with two clinical doctors further validate the model’s potential and interpretability in practical clinical applications.