Abstract: Longline tuna fisheries in the Indian Ocean are commerically important for Chinese offshore fisheries. Water temperature at different depths can affect longline albacore catch rates. In this paper, the generalized additive model (GAM) was used to analyze the relationship between the the catch rate of albacore (Thunnus alalunga) and water temperature at different depths based on the fishery data and corresponding Argo buoy data during 2008−2017. The results show that the catch rate of albacore tuna catch rate was significantly affected by the sea surface temperature (0 m), water temperature at depth of 200 and 400 m. The optimal GAM model explained the variance of catch rate (Catch per unit effort) by 53.3%, and the determining coefficient of model was 0.527. The catch rates of albacore tuna had a nonlinear relationship with the temperature of the three selected water layers. High catch areas were concentrated at the surface layer of 17−30 ℃, 200 m deep sea area of 17−20 ℃, and 400 m deep sea area of 9−15 ℃, and their intersection areas. We have derived the relationship between the spatial distribution of albacore tuna catch rate in the South Indian Ocean and the temperature of three water depth sections for the first time, and the results provide technical support for guiding the rational production of albacore tuna in the Indian Ocean.