Talent training has a strong social constraint and economic dominance, and is closely related to regional economic development with two-way interaction. Context is any information that can be used to describe the situation and characteristics of an object, including time, location, social relationships, natural conditions, and project characteristics. Regional economic development is influenced by the process of multiple types of contextual elements, but traditional development models do not consider or only take into account a single contextual element, ignoring the combined influence of multiple contextual elements. To this end, the paper proposes a model for analyzing the correlation between talent training quality and regional economic development that integrates context-awareness and random forest algorithms, modeling contextual elements as feature attributes to be considered when splitting decision trees in random forests. The experimental results show that when conducting the analysis, assigning corresponding weights to various contextual elements according to the degree of importance can improve the accuracy of the recommendations. The prediction accuracy of the random forest model is higher under different data sampling ratios.