With the rapid development of artificial intelligence technology, the field of accounting information management has put forward new requirements for personalized and precise talent cultivation. Traditional recommendation systems face shortcomings in addressing data sparsity, multi-dimensional feature fusion, and cold start problems, making it difficult for them to adapt to the needs of dynamic educational scenarios. To address this, the research proposes a recommendation model based on hybrid genetic ensemble learning, which combines collaborative filtering, selective ensemble learning, and simulated annealing optimization to effectively enhance the performance and applicability of the recommendation system. Experimental results show that the model performs excellently on multiple key indicators. Specifically, the recommendation accuracy reaches 0.87, the recall rate is 0.83, and the F1 score is 0.85, representing improvements of 29.85%, 43.10%, and 37.10% respectively compared to the baseline model. In the context of the cold start problem, the F1 scores for recommending new users and new positions are 0.77 and 0.73, respectively, demonstrating excellent robustness. Additionally, the model outperforms comparison algorithms in terms of memory usage (650 MB), disk read/write operations (1150 times), and power consumption (0.7 Wh), exhibiting high resource efficiency. In the social impact assessment, it achieves a comprehensive score of 93.2, significantly surpassing industry benchmarks, with outstanding performance particularly in dimensions such as employment promotion, skill enhancement, and economic benefit contribution. Overall, the model designed in this research provides an efficient and reliable recommendation tool for talent cultivation in accounting information management and demonstrates extensive potential in intelligent educational applications.