Infrared target detection system is one of the effective technical means for reliably detecting and identifying high-value targets under the conditions of background radiation and other interferences， and it is widely used in various fields. Infrared weak target detection， as an important part of the system， is still a challenging key core technology at present. In this paper， a method based on low-rank tensor spatial learning is proposed， which preserves the structural integrity of the infrared image while considering the consistency of the sequences in the spatio-temporal continuum. The spatio-temporal tensor block model is obtained through a spatio-temporal sliding window， and the infrared tensor dictionary model is constructed under different scenes using a multi-subspace learning strategy. Finally， an optimization algorithm is used to solve the proposed infrared tensor objective function to obtain the low-rank background and sparse target tensor， and the interested infrared weak targets are detected by reconstructing the image. Experimental results show that the method outperforms other existing detection algorithms for target detection in complex-background environments with high-reflection-induced false alarms and combined strong interference scenarios.