Soft large margin clustering （SLMC） has been proved to achieve better clustering performance and interpretability than other algorithms， such as K-Means. However， when facing large scale distributed data storage， computing involved kernel matrix requires large time cost. One of the effective strategies to reduce this time cost is to use random Fourier feature transform to approximate the kernel function， and the feature dimension on which approximating accuracy depends is often too high， which implies the risk of overfitting. This paper embeds the sparsity into kernel SLMC and combines the alternating direction method of multipliers （ADMM） with SLMC. Finally， we propose a distributed sparse soft large margin clustering algorithm （DS-SLMC） to overcome scalability problem and achieve better interpretability through sparsity.