Abstract: With the rapid development of smart grid and energy internet, the electrical network communication and data processing technology is facing the challenges of massive heterogeneous data, high real-time and complex environmental interference. However, the traditional technology has the problems of feature selection redundancy and limited computing efficiency. Therefore, this paper designs an electrical network communication and data processing technology based on improved random forest algorithm. This article introduces a feature selection mechanism based on mutual information and variance threshold fusion, and improves the decision tree growth strategy of traditional random forests, effectively suppressing the negative impact of low performance on the integrated results. The experimental results show that the technology proposed in this paper has higher accuracy in anomaly detection, lower prediction error in communication delay, significantly improves the performance of anomaly recognition and traffic management, and provides a technical path for communication quality assurance and real-time data analysis in smart grids.