In order to enhance the temporal relevance of retrieval result,the text feature extraction and algorithm of multi-label classification were applied to potential temporal intention classification of literature retrieval. From the perspective of retrieving the classification of potential temporal intentions,an algorithm was proposed to automatically classifiy potential temporal intentions of literature,based on text temporal information extraction and labeled LDA. Firstly,by use of such temporal information,the potential temporal intention of literature retrieval was mapped onto specific temporal categories based on temporal information gained from literature. Secondly,the distribution features of temporal phrases across disciplines were used to optimize the process of label selection of the classification model of labeled LDA in order to reduce the impact of sparsity of temporal information on the learning process of classification features. Finally,the proposed algorithm was compared with other multi-label classification algorithms in specific experiments,and the accuracy of automated classification of potential temporal intentions of literature retrieval was analyzed and evaluated. The result shows that the AUC value of the proposed algorithm reaches 94.3%,which increases approximately 4.3%,compared with the algorithm of ECC (Ensembles of Classifler Chains). In addition,the present algorithm has produced favorable classifying effects in different disciplines. Thus,it is an effective learning method for potential temporal intention of literature retrieval.