In past decades, pattern classification has been intensively explored in machine learning. With the in-depth exploration of machine learning in various applications, new challenges arise, which requests researchers to move from data-driven to domain-driven models by integrating domain knowledge, and to move from static to dynamic models to adapt to the changing environment. This paper proposes an intelligent classification system with following features, to address these requests. Firstly, this system integrates both data association and classification modules. The contextual information extracted from input data is saved as learnt knowledge which is then combined with given expert knowledge in classification. The experimental study shows that this learning process helps to reduce the ambiguity of classification. Secondly, the proposed classifier, i.e. knowledge-based naive Bayes, classifies the incoming data based on both expert knowledge and learnt knowledge. Thirdly, a soft-decision mechanism is adopted in classification algorithm, which can effectively handle overlapping data.