An enhanced framework of quantum approximate optimization algorithm (QAOA) is introduced and the parameter setting strategies are analyzed. The enhanced QAOA is as effective as the QAOA but exhibits greater computing power and flexibility, and with proper parameters, it can arrive at the optimal solution faster. Moreover, based on the analysis of this framework, strategies are provided to select the parameter at a cost of $O(1)$. Simulations are conducted on randomly generated 3-satisfiability (3-SAT) of scale of 20 qubits and the optimal solution can be found with a high probability in iterations much less than $O(sqrt{N})$
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