Chinese notional words are combinatorial and metaphorical in nature, and there is a lack of data sets on Chinese notional word discrimination. As a result, the understanding and discriminative capability of traditional methods for Chinese notional words are still limited in machine reading comprehension tasks. For this reason, a large-scale (600k) Chinese notional word discrimination cloze data set (CND) is constructed. In the dataset, a notional word in a sentence is replaced with a blank placeholder, and the correct answer needs to be selected from the two candidate notional words provided. A baseline model, RoBERTa-based notional word discrimination model (RoBERTa-ND), is designed to select candidate words. The model first extracts semantic information in the context using a pre-trained language model. Second, the semantics of candidate notional words are fused, and the scores of candidate words are computed by a classification task. Finally, the model’s ability to discriminate Chinese notional words is further enhanced by enhancing the model’s perception of locations and orientation information. Experiments show that the model achieves the accuracy of 90.21% on CND, beating mainstream cloze test models such as DUMA (87.59%) and GNN-QA (84.23%). This work fills the gap in the research on Chinese metaphorical semantic understanding and can develop more practical value in improving the cognitive ability of Chinese Quiz Bot. The codes of CND and RoBERTa-ND are open-source: https://github.com/2572926348/CND-Large-scale-Chinese-National-word-discrimination-dataset.