Risk Management Capability Prediction Model Based on Spatial Attention Mechanism Optimized Convolutional Neural Network
Siqi Huang, Jun Luo · Applied and Computational Engineering · 2025
This study proposes an improved convolutional neural network model incorporating spatial attention mechanism, aiming to construct an intelligent assessment system for personal risk management ability. The model effectively synergizes local features with global semantic information by dynamically enhancing the weight allocation of key feature regions, and achieves a balanced improvement of accuracy (0.815), recall (0.815), and precision (0.802) in risk prediction tasks, which is a significant advantage over the traditional decision tree model and the Adaboost integration method (0.543). Experiments show that the spatial attention mechanism not only strengthens the model's ability to capture the spatial correlation of data, but also improves the stability of the classification decision through feature selection optimization, and its excellent performance of the three performance indicators with a difference of less than 0.015 confirms the high consistency of the model in positive and negative sample recognition. Compared with the limitations of traditional machine learning methods in feature interaction modeling, the present model demonstrates stronger noise suppression and sample imbalance adaptation, providing a two-dimensional value for the education field: learners can improve risk management literacy through dynamic assessment, and educators can optimize their practical teaching strategies relying on predictive data. The research results not only validate the technical effectiveness of the attention mechanism in risk assessment tasks, but also establish performance benchmarks for constructing more interpretable lightweight prediction models, which is of great practical significance for promoting the innovation and development of intelligent educational assessment systems.