The advent of Artificial Intelligence (AI) in interior design has revolutionized the efficiency of generating design schemes. However, it poses a significant challenge for designers in making informed decisions amidst a plethora of options, particularly in office space design. Computational aesthetic measurement in this domain is inherently multi-layered and multi-dimensional, encompassing tangible attributes such as form, proportion, color, and material, as well as intangible aspects such as the semantic content and abstract aesthetic experiences including perception, emotion, and behavior. Traditional design approaches often lack a systematic aesthetic measurement methodology, hindering comprehensive assessment and optimization. This paper presents a holistic computational aesthetic measurement framework that leverages deep learning and other AI tools for quantifying these features, thereby providing a robust decision-making foundation for designers. The framework is further implemented into a design toolkit facilitating a more effective AI design workflow and tested through iterations of design workshops with professional designers.