As an factor influencing data quality, out-of-distribution (OOD) data detection plays a critical role in AI quality assurance. This paper presents an advanced OOD detection method based on Granular Computing (GrC) and dictionary learning, specifically designed for detecting textual OOD in natural language processing (NLP) systems. First, informative data structure descriptors (information granules) are generated through GrC, which are aimed to reduce the computation overhead in big data analysis. Next, granular dictionary is constructed from these granules and used to represent original data through dictionary learning and data reconstruction. Finally, OOD detection is formalized by analyzing differences between original and reconstructed data. Finally, the proposed method formulate OOD detection via the difference between original and reconstructed data. Experiments conducted on a sentiment analysis system based on a large language model (LLM) and three OOD datasets are implemented. The constructed granular dictionary is firstly demonstrated to have good representation ability supporting effective OOD detection. Furthermore, the proposed method’s effectiveness, efficiency and scalability in textual OOD detection are validated through comprehensive comparative analysis.