Current paper defects identification methods has two radical problems. First, every current method can only identify one or few defects. Second, current methods can hardly detect the complex paper defects accurately. In view of these problems, based on comprehensive analysis of paper defect features, research and summary of all kinds of paper defect identification methods, Fuzzy fusion device is used to conduct feature layer fusion with some paper defect characteristic values, and combine multiple paper defect identification methods, aiming to achieve more efficient and comprehensive paper defect identification. According to the construction equivalence between RBF Neural Network and fuzzy reasoning, the paper defect features information fusion system on basis of RBFNN has the advantage of simple structure and rapidity. Experiments have shown that the method presented is practicable to identify the primary paper defects accurately, including complex paper defects.