The World Health Organization found that cardiovascular and heart disease causes the highest probability of death in the world. Electrocardiogram (ECG) is an important tool widely used in clinical prevention and diagnosis of cardiovascular and heart disease. Automatic analysis of ECG diagnostic technique can greatly reduce the workload of the cardiologists and improve the efficiency of diagnosis. The classification of ECG Heartbeat is the mainly research direction of ECG automatic analysis as automatic ECG heartbeat classification can improve the diagnostic quality of arrhythmia, especially in the area of dynamic electrocardiogram or the long-term ECG recording. This paper presents an ECG beat classification algorithm, The algorithm uses clusting analysis, mixed with linear classifiers, weighted judgment and physician-assisted classification. Using MIT-BIH-AR arrhythmia database as the raw data and ANSI / AAMI EC57: 1998 / (R) 2003 of AAMI as the standard of classification, the experiment results show that only using clusting analysis, mixed with linear classifiers and weighted judgment, the accuracy rate is 86.60%. After introducing cardiologists-assisted classification, the final accuracy rate is 98.16%.