Learning effective high-order feature interactions is crucial for click-through rate (CTR) prediction in recommender systems. Existing methods that learn meaningful high-order feature combinations by reassembling low-order feature combinations, i.e., 2-order feature cross, suffer from high computational costs to calculate the interaction weight of all pairwise feature interactions. Some deep neural network-based methods can be seen as universal function approximators to potentially learn all kinds of feature interactions, however, it had been proved to be inefficient to approximate the low-order interactions, i.e., 2-order or 3rd-order feature interactions, which may influence the accuracy of CTR prediction task. Based on the above consideration, in this paper, we propose a multi-granularity based Feature Interaction Pruning Networks (FeatNet) for CTR Prediction task. Firstly, FeatNet generates different subsets with a threshold pruning operation to select the meaningful feature combinations on the explicit feature granularity, which enables it to keep the diversity of different feature combinations, and reduce the complexity of high-order feature interactions. Based on the pruned feature subsets, implicit high-order feature interactions are further conducted on the granularity of feature elements, which automatically filters out the invalid feature interactions. Extensive experiments are conducted on two real-world datasets, showing the superiority of FeatNet in CTR prediction.