Leveraging extensive ECG data, intelligent ECG recognition represents a pivotal research focus aimed at supporting physicians in conducting thorough data analysis and diagnosis, thereby enhancing efficiency and mitigating medical resource consumption. In order to solve the problem of feature loss and limited performance of single image and single deep learning algorithm in ECG intelligent recognition, a two-input improved VIT recognition method for ECG rainbow code is proposed. Firstly, a mathematical model is proposed to predict the standard period of ECG, and the potential features of ECG are mined by pumping method to generate ECG rainbow code. Then, a dual input feature extraction module is constructed with convolutional neural network to extract local features of multiple ECG images for fusion to achieve multi-dimensional ECG feature representation and fusion. A VIT coding module is used to pay global attention to fusion features to realize ECG recognition based on multi-feature images as input. The ECG recognition method in MIT-BIH database is used for experiments, and the average accuracy of the proposed ECG recognition method is 99.41%, and the accuracy of the N-type ECG collected in the field is 100%. The experimental results show that the proposed image transformation method can effectively visualize ECG features, and the effect is better than the traditional method. The proposed recognition method can realize ECG recognition effectively and has better performance than other similar methods.