The task of temporal action localization is to recognize the action categories and meanwhile detect the start and end time of each action instance. In this paper, we propose a temporal attention and gaussian weighted anchor-free method, named TG-TAL, for temporal action localization. Rather than using anchors, our method regresses action instances directly with video frames as samples. To better address the variable length of action instance, we introduce a multi-level prediction framework with temporal attention. An additional gaussian weight branch is also defined to enhance the classification performance on low-quality temporal segments. Extensive experiments demonstrate that our method is effective on various datasets. In particular, on THUMOS14, our method outperforms one-stage temporal action localization methods and establishes a new state-of-the-art performance with an mAP(%) of 41.9 at tIoU threshold 0.5. Our method also works with two-stages methods and proposal postprocessing methods. Combined with PGCN, our method surpasses the state-of-the-art methods at tIoU threshold 0.7 and achieves a new state-of-the-art performance of 24.1 in terms of mAP(%) on THUMOS14.