Pattern recognition involves identifying specific patterns or features within the provided data. Social network analysis, fraud detection, biological and medical networks, recommendation systems, telecommunication networks, traffic and transportation networks, computer vision and image processing, and natural language processing (NLP) are among the constantly expanding applications of pattern recognition. Graphs are a potent model utilized in several fields of computer science and technology. This study presents a technique based on graph databases for graphical symbol identification. The suggested method employs graph-based clustering of the graph database, which markedly decreases the computational complexity of graph matching. The suggested algorithm is assessed with a substantial quantity of input hand-drawn images, and the output results indicate that it surpasses previous algorithms.