With the computer accurate estimation of electronic parts defect detection in quality control play an important role in the manufacturing industry. In order to realization electronic parts product appearance quality detection control, one kind of processor based on the intelligent knowledge automatic extraction and system integration modeling was presented This paper proposes a method using an adaptive system to establish the relationship between actual electronic parts defect detection and texture features of the surface image. Uses the fuzzy wavelet extraction image feature, and wavelet function is used as fuzzy membership function. The fuzzy inference is realized by neural network and the shape of membership function can be adjusted in real time. It endues the processor with better capability of learning and self adapt. Based on establishment quality-oriented key characteristics index dynamic adaptability analysis control system, an architecture of intelligent fuzzy neural network combined integrated with quality management module of manufacturing execution system (MES) is presented. It formed a detection product appearance quality of intelligent decision support system. The accurate modeling can effectively estimate electronic parts defect detection. The results of experiments demonstrate that the system can detect product appearance quality perfectly, with a high precision and has the practicality.