With the development of big-data computing, the system generated data becomes larger and more complex. Yet systems like fault monitoring, stock analyzing and health-care require processing these data in nearly real-time. The original data processing methods such as "save-query" and "publish-scribe" cannot handle the large volume of data in that speed. Complex event processing (CEP) is a data processing scheme that executes the user's real-time queries. It continually takes the high volume of raw data input and produces output for the corresponding data stream according to the queries. However in some practical environments, the data from system may generate many new patterns, and the CEP system cannot prepare for each of them. Consequently, an extendable CEP system is needed. Existing CEP work mainly focus on several special types of queries without a high level overview, therefore cannot easily guarantee the overall performances of the system. Though the NFA model poses a universal semantic, the scalability of the NFA model is still under discussed. To address these defects, an operator-based complex event processing model is proposed to support operator extension. In addition, a detailed analysis is conducted on time consumption of operator-based model and an optimal algorithm is presented. Finally, the correctness of optimization solutions is verified by experiments. Contrast experiments show that the optimized tree model is three times faster than open-source project Esper.