Contract cases account for a substantial proportion of daily civil disputes, reflecting a considerable volume. The limited accessibility and cumbersome management of traditional paper contracts have significantly hindered the efficiency of contract execution and dispute resolution. As a computer protocol designed to execute contract terms, smart contracts offer new possibilities for the execution and processing of legal contracts, with advantages such as automated execution, decentralization, and immutability. However, their reliance on strict programming logic, lack of interpretative flexibility, and difficulty in dynamic adjustments after deployment constrain the intentions of contract participants and result in uncertainties regarding legal applicability and binding force. Based on the distinctions between legal contracts and smart contracts, this study proposes four key principles, including grammatical requirements, the non-empowerment principle, validity review, and security criteria, providing a theoretical framework for generating and executing legally effective smart contracts. A smart contract transformation and verification model is further designed to adhere to these four principles. The proposed model enhances the processing of legal contracts expressed as transition systems, prevents re-entry attacks, and converts core and additional specifications into computational tree logic for security property verification. Contract passing verification is automatically converted into smart contracts. The entire transformation process complies with the proposed four principles, ensuring that the resulting smart contracts meet current legal standards and can be regarded as legal contracts. Experimental validation includes a simplified sales contract as a case study, demonstrating its initial and enhanced transition system models, partial verification results, and the representative Solidity code generated. The pre-processing operation yields a high-quality dataset constructed from 270592 samples. Consistency evaluation between contract terms and legal provisions achieves Recall rates of 90.27% at R@1, 97.91% at R@5, and 99.30% at R@10. The feature extraction model, aided by a format conversion tool with nearly 100% fidelity, achieves 91.87% accuracy at the token level, confirming the model’s accuracy and reliability. The findings indicate that the proposed principles are highly feasible, while the transformation and verification model effectively addresses the cumbersome nature of paper contract processing, enhances the convenience and flexibility of legal contract execution and management, and enables smart contracts to obtain legal protection while mitigating potential risks.