To address potential biases caused by the long-term dependence on fishing data from Japan and Korea for the stock assessment of albacore tuna（Thunnus alalunga） in the Southeast Pacific Ocean， this study constructed an abundance index using Chinese longline fishery data.Based on Chinese tuna longline fishery catch data and marine environmental parameters from 2013 to 2022， generalized additive models （GAM） were employed to standardize catch per unit effort （CPUE）， quantifying the effects of latitude， longitude， year， month， environmental factors， and their interactions. The standardized CPUE trends of Chinese and Japanese longline fisheries were compared using ordinary least squares （OLS） regression models. The results show that the maximum interpretation bias of the model for CPUE was 69.8%， and latitude had the most significant effect on CPUE. The areas with higher index are mainly around 20°S-30°S and 100°W-120°W， and the highest index is in 2016， and the highest month is from April to August. The trends of standardized CPUE and nominal CPUE were mostly the same， and both have obvious seasonal fluctuations. Except for 2020， the standardized CPUE was lower than the nominal CPUE. In most years， the standardized CPUE based on China's fishery data has a similar trend to that of the Japanese longline fishery. This study provides a new abundance index information for the stock assessment of albacore tuna in the Southeast Pacific Ocean， and is valuable for further improving the stock assessment.