Web information retrieval based on temporal semantics is widely applied in the retrieval scenarios of dynamic temporal information mining, collective memory and temporal question answering system. By fully utilizing the temporal information contained in the Web documents, we cannot only retrieve the set of texts related to specified lexical semantics at a certain time point but also identify the pattern of theme evolution of contents in each independent information carrier over time. A total of 94 studies related to this topic are retrieved and analyzed. The Chinese search words are “时态语义”, “时间信息检索”, “时间信息抽取”, “检索模型”, and “深度学习”; the English search words are “temporal semantic”, “temporal information retrieval”, “temporal information extraction”, “retrieval model” and “deep learning”. The databases searched are Wanfang Database, CNKI, PQDD ProQuest Digital Dissertation, ISI Web of Knowledge, Elsevier ScienceDirect, Academic Source Premier EBSCO, LISA, SpringerLink and EI Engineering Village 2. The extraction and labeling of text's temporal information and the retrieval model incorporated with temporal information are used. Existing studies are classified by comparison and content analysis and are reviewed in terms of each step of information retrieval in the narrow sense. Related researches in three aspects, namely, implicit temporal intention analysis in information requirement, construction of retrieval model incorporated with temporal factors, and promotion of retrieval result generation by temporal information are comprehensively reviewed under the framework of temporal semantics technology. Firstly, basic researches on automatic extraction of text's temporal information and retrieval model incorporated with temporal information are reviewed. A special focus is placed on studies related to temporal perception, retrieval and cognition, temporal query intent algorithms, methods to determine the temporal dimension of literature, temporal-based text similarity computing, and temporal ranking model. We also summarize four major future research directions in the field of Web information retrieval based on temporal semantics: multi-dimension time expression extraction, acquisition of implicit temporal intent, similarity computing in deep level temporal retrieval model, and temporal intent classification based on deep learning. The major limitation of the present review is that besides the overall analysis of theories on Web information retrieval based on temporal semantics, there is also a need for systematic review of relevant theories and concepts. A systemic and comprehensive review of existing studies about Web information retrieval based on temporal semantics from the perspective of key technology is not only beneficial for knowing the research progress and status quo of this field but also helpful to grasping the future development trend of temporal retrieval in the era of big data and artificial intelligence. Such review can help us develop ways to incorporate the technique, methodology and concept of deep learning into information retrieval based on temporal semantics. Originality of this paper is manifested in two aspects. Firstly, key technology involved in Web information retrieval based on temporal semantics is taken as the entry point. Then we perform a systematic and comprehensive review on studies related to information extraction, organization, mining and presentation based on temporal semantics. Secondly, the future development trend of information retrieval based on temporal semantics is pointed out after literature review and a brief analysis on the latest development in this field is carried out. 91 refs.