Similarity calculation is a key step in the user-based collaborative filtering algorithm. As the number of users increases, the similarity computing space will become increasingly tremendous. At the same time, the accuracy is relatively low when it is applied to the agricultural personalized recommendation system. According to the feature of agricultural materials which are strongly influenced by seasons and locations, TA-ACF(Agricultural collaborative filtering algorithm based on both time and area) is proposed based on time and area size, which improves the original similarity calculation method. In this way, these above-mentioned problems could be solved. The main idea is to establish the matrix of time and size according to the existing research of the agricultural demands, and establish a rating matrix within the time and size. As the result shows, compared to the user-based collaborative filtering algorithm, TA-ACF is able to improve the quality of recommendations without losing time efficiency.