Symmetric alternating direction method of multipliers （S-ADMM） is an efficient method for convex optimization problems with separable structure. The algorithm makes use of the separability of the objective function to decompose the original problem into several minimization subproblems and to solve them alternately.Whether the subproblems can be effectively solved affects the effectiveness of the algorithm. In many practical applications, subproblems cannot be solved precisely, or the cost of solving subproblems precisely is relatively high. To solve this problem, a modified symmetric alternating direction method of multipliers (MS-ADMM) is proposed. Compared to the general symmetric ADMM, this algorithm adds a semi-proximal term to x-subproblem which is then solved approximately. This overcomes the shortcoming of the previous algorithm. The convergence of the sequence generated by the proposed algorithm is proved under some suitable assumptions. Preliminary numerical experiments illustrate the effectiveness of proposed algorithm.