Explainable artificial intelligence(XAI) is growing rapidly in recent years and many interpretability techniques have emerged, but there is a lack of quantitative evaluation approaches for XAI's interpretability. Most of existing evaluation methods rely on users' experiments, which is time-consuming and costly. Aiming at the surrogate model-based XAI, we propose a quantitative evaluation approach for the XAI's interpretability. Firstly, we devise some indices for this kind of XAI and give their computational method, and construct an index system with 10 quantitative indices to evaluate the XAI's interpretability from five dimensions, namely consistency, user comprehension, causality, effectiveness and stability. For the dimension with multiple indices, a comprehensive evaluation model is established by combining the entropy weight method with TOPSIS to evaluate the XAI's interpretability in the dimension. The proposed approach is applied to the evaluation of the interpretability of 6 XAIs based on the rule surrogate model. Experimental results show that the approach can demonstrate the XAI's interpretability in different dimensions, and users can choose suitable XAI according to their needs.