Abstract: 【Purpose/significance】To address the issue where the lack of explanations for friend recommendation results hinders users from assessing the quality of the recommended friends, thereby impacting their ability to make better decisions.【Method/process】The process begins with the extraction of user blog topics using the LDA model, followed by the calculation of blog content similarity through cosine similarity; friend similarity is determined based on the proportion of mutual friends; and research field similarity is com⁃ puted using the Jaccard algorithm. These three types of similarities are then integrated to calculate an overall user similarity. Textual explanations based on blog topics, mutual friends, and research fields are subsequently designed. Finally, user similarity and textual explanations are merged to provide textual explanations alongside the friend recommendation list.【Results/conclusion】The model not only enhances the accuracy of friend recommendations but also aids users in making more informed decisions by providing explana⁃ tions, thus improving the quality of friend recommendations and increasing user satisfaction.【Innovation/limitations】The innovation of this study lies in the introduction of explainability into the realm of friend recommendation, which enhances users' understanding and acceptance of the recommendation results, enabling them to make better decisions. However, the impact of the length of textual ex⁃ planations on their effectiveness was not considered, which will be further explored in future research.