. Moreover, most existing methods assume the decision boundary is local linearity, which may produce serious errors as in most complex networks, the local decision boundary is non-linear.  In this paper, we design and develop a novel, high-interpretability and high-fidelity local explanation method to address the above challenges. First, we design a unique local sampling process which incorporate the feature clustering method to handle the feature dependency problems. Then, we adopt S