Electrocardiography is a common tool for detecting cardiovascular system diseases. In clinical, as the individual difference is an intrinsic feature of ECG, data distribution difference between training and testing data impacts on the accuracy of classifier. Automatic ECG classification satisfied clinical demand is urgently required. QRS is a main waves in a heartbeat. In this paper, we propose a complete framework for individual oriented QRS morphology variation analysis. The original signal is first preprocessed by re-sampling and smoothing, then symbolized by dynamic and static combined method. For similarity measure, an improved information entropy measure function based on the symbolic result is proposed and ECG domain knowledge is well utilized by the function. At last, the entropy function based unsupervised learning algorithm is presented for QRS complex similarity computation. Our algorithm dedicates to the individual data analysis combined with domain knowledge, which is free from any training data and more suitable for application. Comprehensive experiments show that the proposed entropy function achieves improvements over the general distance measure functions during QRS similarity measure. The clustering algorithm is effective at recognizing normal and abnormal QRS morphology.