Due to the limited resources of edge networks, the heterogeneity of user content requests, high-cost caching from direct resource hits, and redundancy in resource retention time hinder system performance. Traditional caching methods based on learning and popularity tend to encounter data skew when handling vehicle requests, leading to untimely cache data updates. To address these issues, this paper proposes an efficient edge cooperative caching strategy with recommendation awareness. Firstly, a time series prediction model based on a multi-dense multilayer perceptron is designed. This model segments historical request information into sub-sequence level slivers to enhance positional correlations between information, predicting user requests through historical time series and covariates. Secondly, to optimize cache utilization, comprehensively considers user mobility and contribution to enable recommendation awareness. Additionally, an optimized lifecycle is introduced to design a cache replacement strategy. Simulation experiments demonstrate that, compared to caching schemes based on content popularity and deep reinforcement learning, the proposed method achieves lower caching costs and higher cache hit rates.