Abstract: Electrocardiogram (ECG) is a non-invasive, low-cost and widely used method for cardiac detection. It has played a significant role in cardiovascular disease screening and diagnosis for a long time. Traditional ECG analysis relies on manual interpretation, which limits its efficiency and accuracy. In recent years, the rapid development of artificial intelligence (AI), especially deep learning technology, is profoundly changing the paradigm of ECG analysis. Convolutional neural networks and Transformer models have performed well in arrhythmia detection, and some models' accuracy has surpassed that of human experts, promoting the intelligence of clinical auxiliary diagnosis. In the early screening of heart diseases, AI models can identify subtle abnormalities in normal ECGs and predict hidden diseases such as left ventricular systolic dysfunction, left ventricular hypertrophy, and prolonged QT interval, helping with individualized prevention. In signal processing, generative adversarial networks and autoencoders have demonstrated significant advantages in ECG signal denoising and multi-channel reconstruction, improving the data quality of remote medical care and wearable devices. Multi-modal fusion platforms integrate ECG, clinical features, and medical images to enhance the accuracy of risk prediction and the practicality of the model. To enhance interpretability, methods such as Grad-CAM and SHAP are used to reveal the basis of model decisions, improving doctors' trust. At the same time, to enhance generalization ability, researchers introduce strategies such as transfer learning, federated learning, and domain adaptation to enhance the model's adaptability in different populations and device scenarios. Therefore, this article presents the latest progress of AI in the field of ECG intelligent analysis, with the aim of promoting the development of AI in ECG analysis towards more intelligent, precise, and diversified directions, providing important support for the early identification and individualized treatment of cardiovascular diseases, and having broad clinical translation prospects.