The increasing complexity of high performance computer system architecture and inadequate intelligence of the existing performance analysis tools, lead to the increasing cost for performance analysis and optimization of high performance computing applications. Thanks to the deep learning technology, significant progress in the field of artificial intelligence has been made, and it also presents an opportunity for intelligent program performance analysis tools. We propose an intelligent program performance analysis framework, which abstracts program performance analysis into a classification problem via machine learning technology. The performance data, the input of the analysis framework, which is collected via the performance monitoring unit, is standardized in advance; and the categories of performance problems, the output of the analysis framework, are determined by the cluster evaluation and the actual meaning of every cluster. Sparse coding is used to automatically learn the features of performance data and a performance problem classification model is built. Finally, we implement the prototype of the framework on the Sunway TaihuLight system. Experimental results show that this framework can intuitively guide programmers to grasp the most prominent performance bottleneck problem in current applications, effectively improve the performance analysis efficiency, and reduce the cost of code tuning.