Complex action recognition aims to identify multiple actions over a long time. Multiple actions may occur at the same time (defined as simultaneous actions), and may occur after each other (defined as each action) Complex action recognition may suffer from two challenges. (1)Temporal repeated bias.The same action may repeat in a temporal duration. In this duration, the prediction may be biased to the majority of actions, which occur repeatedly in the past temporal frames. (2)Epistemic uncertainty of multiple actions.When there are multiple simultaneous actions in one frame, this frame's feature may result in the distribution of multiple actions overlapping each other. Without modeling proper relations between actions, the model may hinder accurately explaining certain categories in multiple actions (defined as the model's epistemic uncertainty). In this work, we propose anInstructive Probabilistic Transformer, which contains a probabilistic temporal memorizer, and a probabilistic prototype Transformer.First, to alleviate temporal repeated bias, we design a probabilistic temporal memory module, which learns probabilistic temporal gates to localize each action. The probabilistic gates instruct the selective memory of each action in long-term frames.Second, we cluster features to capture common action semantics among features (defined as action prototypes). To alleviate the epistemic uncertainty of multiple actions, we design a probabilistic prototype Transformer module. This module learns probabilistic relations depending on each prototype, which can ensure the separation between different prototypes.Third, to ensure the proper probabilistic relations depending on each prototype, we extend action loss with distribution loss to learn uncertainty-aware action loss. In uncertainty-aware action loss, the distribution loss measures the consistency between probabilistic relations and prototype relation distribution. The prediction uncertainty is learned by analyzing the entropy of multiple predictions, and helps to ensure the effect between action loss and distribution loss. Extensive experiments demonstrate that our method achieves state-of-the-art performance on Charades, Breakfast Actions, and MultiTHUMOS.