Low-Carbon Awareness Information Technology of Enterprise Executives Based on Big Data and Multimodal Information Fusion

Abstract

The so-called multimodal information refers to the information from different information sources on different or the same side of the same description target. These pieces of information are different in terms of storage structure, representation, semantic connotation, credibility, and emphasis, but there is a certain inevitable connection between them. This paper aims to study how to analyze and study the low carbon of enterprises with the help of multimodal information fusion based on the background of big data and construct the evolutionary neural network of the improved adaptive genetic algorithm. This paper puts forward the problem of low carbon in enterprises, which is based on multimodal information fusion, and then elaborates on the concept and related algorithms of multimodal information fusion. Information fusion has carried out case design and analysis. Through the research and analysis of enterprise low carbon and self-adaptive algorithm, it can be obtained that the neural network has reached the threshold of 3.95 after iterating for nearly 60 generations, and stopped iterating to obtain the best individual. The evolutionary neural network in this paper reaches stability after a small number of iterations and can basically achieve a certain low carbonization.