User Perception Analyzation System (UPAS) plays important supportive role in maintaining every step of network operation and maintenance for communication carrier. However, the consistently changing of network and its relevant businesses bring forth great challenge to the perception analyzation. Based on the UPAS theory, this research first investigated its possible drawbacks on benefit and cost, and proposes a two-stage user perception measurement and analysis framework. In the first stage, a general quantitative evaluation model is constructed for evaluating single service quality. In the second stage, a quasi-unsupervised machine learning model is constructed, so that the satisfaction evaluation method has the ability of self-learning to adapt to the dynamic changes of the network. It can reduce the shortcomings of the network to specific cells and specific services that can greatly improves the practicability and usability of the method. The analysis from the existing network shows that the recall rate and precision rate of this method are much higher than those of the traditional method. Finally, the future evolution of the system in capacity, efficiency and operation are viewed.