Subgraph matching is a fundamental task in graph analysis systems. In real-world applications, graph query engines often need to process a large number of subgraph matching queries, many of which share common substructures. Materializing the results of these shared subqueries as view patterns can enable computation reuse and significantly improve query efficiency. However, existing view materialization techniques suffer from either high memory usage or limited acceleration benefits.This paper introduces MAVIS, a novel view-based subgraph matching algorithm. MAVIS partitions view patterns into connected subgraphs called super-nodes and performs super-node-oriented materialization to balance memory consumption and processing speed. To further improve efficiency, it proposes a tree-based super-node partitioning method that avoids generating invalid candidates during materialization. Additionally, a customized query answering algorithm is designed to leverage the materialized views for faster query execution.Extensive experiments on real-world datasets demonstrate that MAVIS achieves a superior trade-off between memory usage and acceleration, and it outperforms existing approaches.