Session-based recommendation aims to predict the user’s next click behavior based on the click sequence information of the current user’s anonymous session. Most of the existing methods realize recommendations by modeling the item information of the user’s session click sequence and learning the vector representation of the items. As a kind of coarse-grained information， the item category information can aggregate the items and can be used as an important supplement to the item information. Based on this， a Session-based Recommendation model of Multi-granular Graph Neural Network （SRMGNN） was proposed. Firstly， the embedded vector representations of items and item categories in the session sequence were obtained by using the Graph Neural Network （GNN）， and the attention information of users was captured by using the attention network. Then， the items and item category information given by different weight values of attention were fused and input into the Gated Recurrent Unit （GRU）. Finally， through GRU， the item time sequence information of the session sequence was learned， and the recommendation list was given. Experiments performed on the public Yoochoose dataset and Diginetica dataset verify the advantages of the proposed model with the addition of item category information， and show that the model has better effect compared with all the eight models such as Short-Term Attention/Memory Priority （STAMP）， Neural Attentive session-based RecomMendation （NARM）， GRU4REC on the evaluation indices Precision@20 and Mean Reciprocal Rank （MRR）@20.