Symbolic execution is an essential approach for automated test case generation. However, the approach is generally not scalable to large programs. One critical reason is that the constraint solving problems in symbolic execution are generally hard. Consequently, the symbolic execution process may get stuck in solving such hard problems. To mitigate this issue, symbolic execution tools generally rely on a timeout threshold to terminate the solving. Such a timeout is generally set to a fixed, predefined value, \textit{e.g.}, five minutes in angr. Nevertheless, how to set a proper timeout is critical to the tool's efficiency. This paper proposes an approach to tackle the problem by predicting the time required for solving a constraint model so that the symbolic execution engine could base on the information to determine whether to continue the current solving process. Due to the cost of the prediction itself, our approach triggers the predictor only when the solving time has exceeded a relatively small value. We have shown that such a predictor can achieve promising performance with several different machine learning models and datasets. By further employing an adaptive design, the predictor can achieve an F1-score ranging from 0.743 to 0.800 on these datasets. We then apply the predictor to eight programs and conduct simulation experiments. Results show that the efficiency of constraint solving for symbolic execution can be improved by 1.25x to 3x, depending on the distribution of the hardness of their constraint models.