Network Public Opinion Information Resource Sharing Method Based on Artificial Intelligence Algorithm

The network public opinion information resources include text, pictures, videos and other modes, resulting in high sharing loss value. The network public opinion information resources sharing method is based on data analysis and artificial intelligence algorithm. First, based on spatial theory, a spatial model of the emotional dimension of network public opinion big data is constructed to dynamically capture and express the multi-dimensionality and dynamism of public opinion emotions. Subsequently, advanced multimodal neural network technology was utilized to accurately identify and extract deep features of network public opinion information resources, effectively addressing data heterogeneity. Furthermore, designing and implementing a resource sharing mechanism based on semantic fusion algorithm promote efficient matching and sharing of resources through deep semantic alignment and composite semantic relationship mining. Finally, simulation tests were conducted from four aspects: data analysis, shared loss values, feature recognition effectiveness, and shared performance. The results showed that the proposed method performed well in quantitative experiments, with lower sharing loss values (about 0.01), more accurate identification of network public opinion big data features, and significantly shorter sharing completion time, average waiting time, and resource download time than the comparative methods, only 7.66s, 2.03s, and 5.04s, respectively, proving its stronger sharing ability and superior performance.