Simplicial complexes play a critical role in higher-order network analysis due to their heterogeneity, but identifying influential simplices remains elusive. This paper proposes the influential simplices mining neural network (ISMnet), which uses higher-order presentations and graph convolutional operators to identify vital simplices of arbitrary order. Empirical results demonstrate that ISMnet significantly outperforms existing methods in ranking nodes and 2-simplices, making it a potent tool in higher-order network analysis.