In this paper, I propose Yul2Vec, a novel method for representing Yul programs as distributed embeddings in continuous space. Yul serves as an intermediate language between Solidity code and Ethereum Virtual Machine (EVM) bytecode, designed to enable more efficient optimization of smart contract execution compared to direct Solidity-to-bytecode compilation. The vectorization of a program is achieved by aggregating the embeddings of its constituent code elements from the bottom to the top of the program structure. The representation of the smallest construction units, known as opcodes (operation codes), along with their types and arguments, is generated using knowledge graph relationships to construct a seed vocabulary, which forms the foundation for this approach. This research is important for enabling future enhancements to the Solidity compiler, paving the way for advanced optimizations of Yul and, consequently, EVM code. Optimizing EVM bytecode is essential not only for improving performance but also for minimizing the operational costs of smart contracts - a key concern for decentralized applications. By introducing Yul2Vec, this paper aims to provide a foundation for further research into compiler optimization techniques and cost-efficient smart contract execution on Ethereum. The proposed method is not only fast in learning embeddings but also efficient in calculating the final vector representation of Yul code, making it feasible to integrate this step into the future compilation process of Solidity-based smart contracts.