Multi-Dimensional Data Evaluation and Prioritisation based on Intelligent Decision-Making Algorithm

In the era of big data analysis, multi-dimensional data evaluation and priority ranking become the key issues of intelligent decision-making systems. Traditional methods are difficult to meet the needs of refined evaluation and dynamic priority adjustment, and the sorting learning method in machine learning is gradually applied. In this paper, we construct a comprehensive evaluation framework integrating deep sorting model and index system weight analysis, introduce Listwise learning algorithms such as ListNet, and combine with deep learning technology to achieve global sorting optimisation and decision-making of objects under multi-dimensional data. Taking the evaluation of Olympic projects as an example, the weights of indicators are determined by entropy weighting method, TOPSIS method is applied for evaluation, and indicators such as NDCG and MAP are used to assess the performance of the model. The experimental results show that the proposed model significantly outperforms the traditional methods in terms of sorting performance, which improves the scientificity and practicality of the decision-making system.