Heterogeneous Network Multi Ecological Big Data Fusion Method Based on Rotation Forest Algorithm

Nowadays, with the rapid development of economy and science and technology, the ecological environment has been greatly affected. In recent years, the concept of sustainable development has been paid more and more attention, and the protection of ecological environment is becoming more and more important. With the advent of the era of big data, ecological data also presents a trend of diversification. The integration of multi ecological big data is conducive to solving more and more serious ecological problems. Therefore, this paper proposes a multi ecological large data fusion method based on rotation forest algorithm for heterogeneous network. In the study, we introduced rotation forest into data fusion, selected an ecological region as the research area, and selected 500 samples from 2000 samples as test sets. In this paper, the proposed method is verified from three aspects of fusion confidence, overall accuracy and computational time efficiency, and is compared with the other two methods. The results show that the highest fusion confidence of method 1 is 0.8, that of method 2 is 0.65, and that of this method is 0.92. In addition, in terms of overall accuracy and computational efficiency, the proposed method based on rotation forest algorithm has more advantages than the other two methods.