Current college student job recommendation methods based on collaborative filtering and latent factor model only consider job interests of students and ignore the requirements of employers, often leading to ‘capability mismatch’. Moreover, in most of the historical employment data, only one employment record per student is stored, which leads to unreliable negative samples and affects recommendation performance. Additionally, many methods ignore the demand for recommendation result interpretability. To this end, inspired by the idea of multi-task learning, we construct a reciprocal-constrained interpretable job recommendation method. In which, we introduce attention mechanism to extract bidirectional preferences of both students and employers, and then use fuzzy gate mechanism to adaptively aggregate them in order to alleviate the problem of capability mismatch. Next, we propose a recommendation interpretation module oriented to employer intention and employer characteristics to meet the interpretability demand. We also propose a similarity-based negative sampling method to solve the problem of incredible negative samples. The results of experiment on a real-world undergraduate employment dataset of five years, EMDAU, indicate that our method outperforms other classic and state-of-art recommendation methods and has over 6% improvement in AUC. Besides, the results of ablation experiments conducted verify the effectiveness of each module in our method.