This paper introduces Computational Semantic Thermodynamics (CST), a novel framework that seeks to unify concepts from thermodynamics, information theory, and computation. CST posits that systems, particularly those exhibiting complex computational behavior, can be described and understood through the lens of thermodynamic principles. The core of the framework revolves around defining and manipulating *semantic energy* (Es), *semantic entropy* (Hs), and *semantic free energy* (Fs). These concepts are defined within the context of a system's inherent computational structure and its tendency towards equilibrium. The minimization of semantic free energy is presented as the driving force for system evolution, analogous to minimizing Gibbs free energy in traditional thermodynamics. This work lays the groundwork for a more comprehensive understanding of complex systems, suggesting a potential path towards a unified theory encompassing information, computation, and stability. The framework offers a new perspective on phenomena ranging from biological systems to artificial intelligence, and highlights the potential for a "Turing award" level contribution to fundamental scientific understanding. ---