Abstract: To optimize the sequential checking and update operations during dual simulation, this paper proposes a graph pattern-matching algorithm based on Algebraic Decision Diagram (ADD), namely GSDD, which optimizes the sequential checking and update operations during matching execution. The proposed GSDD algorithm employs algebraic decision diagrams to construct symbolic representations of both pattern graphs and data graphs. The algorithm performs symbolic set operations in memory, allowing multiple candidate nodes to be processed in parallel rather than sequentially. A novel label-constrained filtering strategy is further introduced to jointly process a node and its immediate neighbours, thereby improving candidate set pruning in dual simulation. Subsequently, a backtracking strategy is adopted to further refine the candidate node set. Experimental results demonstrate that the proposed GSDD algorithm effectively improves graph pattern matching efficiency compared with the conventional dual simulation method.