This paper proposes a variable step-size accelerated proximal gradient algorithm for low-rank matrix completion. The method employs an adaptive step-size selection strategy based on the Armijo rule, which ensures a monotonic decrease of the objective function per iteration and enhances computational efficiency. The convergence of the algorithm is rigorously established under standard assumptions. Numerical experiments demonstrate the algorithm's superiority over existing methods in terms of both convergence speed and overall efficacy.