The Quantum approximate optimization algorithm is a quantum-classical hybrid algorithm, which can obtain the optimal solution of combinatorial optimization problems in polynomial time. Unfortunately, at a low level of iteration, there is no guarantee that the problem will be solved optimally. This study develops a quantum circuit with fewer quantum gates based on the revised target Hamiltonian to address this difficulty, which streamlines the solution procedure and boosts solution precision. In order to verify the reliability of the proposed solution, a large number of experiments have been carried out by solving the Integer Linear Programming problem (ILP). The experiment is deployed in the PyQpanda environment of the Origin Quantum. The findings show that the average execution time is 20.8% of the original, and the probability is enhanced from 54.1563% to 82.9%.