Large　language　models　(LLMs)　have　shown　nearly　saturated　performance　on　many　natural　language　processing　(NLP)　tasks.　As　a　result,　it　is　natural　for　people　to　believe　that　LLMs　have　also　mastered　abilities　such　as　time　understanding　and　reasoning.　However,　research　on　the　temporal　sensitivity　of　LLMs　has　been　insufficiently　emphasized.　To　fill　this　gap,　this　paper　constructs　Multiple　Sensitive　Factors　Time　QA　(MenatQA),　which　encompasses　three　temporal　factors　(scope　factor,　order　factor,　counterfactual　factor)　with　total　2,853　samples　for　evaluating　the　time　comprehension　and　reasoning　abilities　of　LLMs.　This　paper　tests　current　mainstream　LLMs　with　different　parameter　sizes,　ranging　from　billions　to　hundreds　of　billions.　The　results　show　most　LLMs　fall　behind　smaller　temporal　reasoning　models　with　different　degree　on　these　factors.　In　specific,　LLMs　show　a　significant　vulnerability　to　temporal　biases　and　depend　heavily　on　the　temporal　information　provided　in　questions.　Furthermore,　this　paper　undertakes　a　preliminary　investigation　into　potential　improvement　strategies　by　devising　specific　prompts　and　leveraging　external　tools.　These　approaches　serve　as　valuable　baselines　or　references　for　future　research　endeavors.　©　2023　Association　for　Computational　Linguistics.