Research on Search Optimization and Knowledge Discovery Methods for Operator in Multi-Source Heterogeneous Data Fusion

With the rapid development of multi-source heterogeneous data fusion technology poses a new demand for efficient search optimization and knowledge discovery, especially in complex application scenarios related to operator data, which has become an important research field. We originally design a multimodal data adaptive fusion model by combing the multi-level deep learning network with the graph neural network, thus a novel method based on the existed data fusion and search optimization model is proposed in this paper. We not only extract latent correlation features from multi-source data but also combine them with search optimization algorithms, which enhances the improvement on the accuracy and efficacy of data fusion. In addition, a dynamic weight adjustment mechanism is introduced into the model so as to adapt to the variation of different data sources and task requirements, and thus optimize the process of knowledge discovery. The experimental results indicate that the proposed model is better than generally trained methods in accuracy and processing efficiency from the aspects of search optimization and knowledge discovery in operator data processing.