Optimization of Big Data Search Technology in Intelligent Systems

With the popularization of intelligent systems, traditional big data search methods have problems such as low efficiency and insufficient information retrieval accuracy when facing massive data, and they need to be optimized urgently. In response to these challenges, this paper proposes a big data search optimization solution based on Graph Neural Network (GNN). First, this paper directly models the nodes and their associations in the data by constructing a graph neural network model. Then, this paper proposes an adaptive weight adjustment mechanism that enables the search process to be dynamically adjusted according to real-time data. Finally, by optimizing the network structure, GNN achieves efficient processing of complex data structures and ensures the efficient operation of the system. In the experimental conclusion, the proposed GNN optimization scheme has significant advantages in search speed, accuracy and system stability. For example, in the test of the Cora dataset, the search accuracy of the GNN method is 0.87 and the recall rate is 0.82. In terms of search speed, as the number of queries increases, the response time of GNN remains within a reasonable range. The system stability test results also show that GNN can maintain a low error rate and crash frequency in a high-concurrency environment. From the above data conclusions, the application of GNN in big data search can effectively improve the performance of intelligent systems and has great innovation potential and practical value.