The recent technique of Model-Based Reinforcement Learning learns to make decisions by building a world model about the dynamics of the environment. The world model learning requires extensive interactions with the real environment. Therefore, several innovative approaches such as APV proposed to unsupervised pre-train the world model from large-scale videos, allowing fewer interactions to fine-tune the world model. However, these methods only pre-train the world model as a video predictive model without action conditions, while the final world model is action-conditional. This gap limits the effectiveness of unsupervised pre-training in enhancing the world model's capabilities. To further release the potential of unsupervised pre-training, we introduce an approach that Pre-trains the world model from action-free videos but with Learnable Action Representation (PreLAR). Specifically, the observations of two adjacent time steps are encoded as an implicit action representation, with which the world model is pre-trained as action conditional. To make the implicit action representation closer to the real action, an action-state consistency loss is designed to self-supervise its optimization. During fine-tuning, the real actions are encoded as the action representation to train the overall world model for downstream tasks. The proposed method is evaluated on various visual control tasks from the Meta-world simulation environment. The results show that the proposed PreLAR significantly improves the sample efficiency in world model learning, demonstrating the necessity of incorporating action in the world model pre-training.