Long non-coding RNAs (lncRNAs) are primarily regulated by their localization within the cell, which is responsible for their molecular functions, including cell cycle regulation and genome rearrangements. Accurately identifying the subcellular location of lncRNAs from sequence information is crucial to better understand their biological functions and mechanisms. Contrary to traditional experimental methods, bioinformatics or computational methods can be applied for the annotation of lncRNA subcellular locations from the human more effectively. In the past, several machine learning-based methods have been developed to identify lncRNA subcellular localization, but the relevant work for identifying cell-specific localization of human lncRNA remains limited. In this study, we present the first application of tree-based stacking approach named TACOS, to allow users to identify subcellular localization of human lncRNA for ten different cell types. More specifically, we conducted comprehensive evaluations of six tree-based classifiers with ten different feature descriptors using a newly constructed balanced training dataset for each cell type. Following that, AdaBoost baseline model’s strengths were integrated through a stacking approach with an appropriate tree-based classifier for the final prediction. TACOS displayed consistent performance on both the cross-validation and independent assessments compared to other two approaches employed in this study. A user-friendly online TACOS web server can be accessed at https://balalab-skku.org/TACOS.