Time-series data exhibits recency characteristic， i.e.， variable values are generally dependent on recent historical information. Existing time-series causal inference methods do not fully consider the recency characteristic， which use a uniform threshold when inferring causal relationships with different delays through hypothesis testing， so that it is difficult to effectively infer weaker causal relationships. To address the aforementioned issue， a method for time-series causal inference based on adaptive threshold learning was proposed. Firstly， data characteristics were extracted. Then， based on the data characteristics at different delays， a combination of thresholds used in the hypothesis testing process was automatically learned. Finally， this threshold combination was applied to the hypothesis testing processes of the PC （Peter-Clark） algorithm， PCMCI （Peter-Clark and Momentary Conditional Independence） algorithm， and VAR-LINGAM （Vector AutoRegressive LINear non-Gaussian Acyclic Model） algorithm to obtain more accurate causal relationship structures. Experimental results on the simulation dataset show that the F1 values of adaptive PC algorithm， adaptive PCMCI algorithm， and adaptive VAR-LINGAM algorithm using the proposed method are all improved.