The session-based recommendation algorithm based on Recurrent Neural Network （RNN） can effectively model the long-term dependency in the session， and can combine the attention mechanism to describe the main purpose of the user in the session. However， it cannot bypass the items that are not related to the user’s main purpose in the process of session modeling， and is susceptible to their influence to reduce the recommendation accuracy. In order to solve problems， a new paired coding scheme was designed， which transformed the original input sequence embedding vector into a three-dimensional tensor representation， so that non-adjacent behaviors were also able to be linked. The tensor was processed by a two-dimensional Conventional Neural Network （CNN） to capture the relationship between non-adjacent items， and a Neural Attentive Recommendation Machine introducing two-dimensional COnvolutional neural network for Session-based recommendation （COS-NARM） model was proposed. The proposed model was able to effectively skip items that were not related to the user’s main purpose in the sequence. Experimental results show that the recall and Mean Reciprocal Rank （MRR） of the COS-NARM model on multiple real datasets such as DIGINETICA are improved to varying degrees， and they are better than those of all baseline models such as NARM and GRU-4Rec+. On the basis of the above research， Euclidean distance was introduced into the COS-NARM model， and the OCOS-NARM model was proposed. Euclidean distance was used to directly calculate the similarity between interests at different times to reduce the parameters of model and reduce the complexity of model. Experimental results show that the introduction of Euclidean distance further improves the recommendation effect of the OCOS-NARM model on multiple real datasets such as DIGINETICA， and makes the training time of the OCOS-NARM model shortened by 14.84% compared with that of the COS-NARM model， effectively improving the training speed of model.