Code representation aims to extract the characteristics of source code to obtain its semantic embedding, playing a crucial role in deep learning-based code intelligence. Traditional handcrafted code representation methods mainly rely on domain expert annotations, which are time-consuming and labor-intensive. Moreover, the obtained code representations are task-specific and not easily reusable for specific downstream tasks, which contradicts the green and sustainable development concept. To this end, many large-scale pretraining models for source code representation have shown remarkable success in recent years. These methods utilize massive source code for self-supervised learning to obtain universal code representations, which are then easily fine-tuned for various downstream tasks. Based on the abstraction levels of programming languages, code representations have four level features: text level, semantic level, functional level, and structural level. Nevertheless, current models for code representation treat programming languages merely as ordinary text sequences resembling natural language. They overlook the functional-level and structural-level features, which bring performance inferior. To overcome this drawback, this study proposes a representation enhanced contrastive multimodal pretraining (REcomp) framework for code representation pretraining. REcomp has developed a novel semantic-level to structure-level feature fusion algorithm, which is employed for serializing abstract syntax trees. Through a multi-modal contrastive learning approach, this composite feature is integrated with both the textual and functional features of programming languages, enabling a more precise semantic modeling. Extensive experiments are conducted on three real-world public datasets. Experimental results clearly validate the superiority of REcomp.