摘要：The traditional comprehensive evaluation is difficult to model when dealing with large data with large parameters and complex structure, and it cannot adapt to the update of data. In order to improve this situation, this paper draws on the Adaptive Learning Adaboost perspective in statistical learning to develop a data-driven integrated evaluation model that updates the weight of sample weights and weak evaluation models with data. Three specific weak evaluation models were selected: data-driven Topsis method, principal component analysis method and factor analysis method. Taking the ranking of WeChat public account as an example, the results show that the accuracy of the integrated evaluation model is 88.57%, which is 17.14%, 31.43% and 28.57% higher than the data-driven Topsis method, principal component method and factor analysis method.
