Abstract: With the rapid progression of artificial intelligence, social science research is experiencing an unparalleled transformation. This paper delves into how AI is invigorating various facets of the social science research through the incorporation of innovative technologies, newly-built correlations, and novel models, thereby elucidating the consequent effects and shifts. Specifically, this paper first evaluates how AI permeates key stages like the acquisition of academic resources, the organization of research findings, the linking of research subjects, the evolvement of research themes, and the submission of academic papers. It highlights that AI technologies amplify research efficiency, expand scholarly viewpoints, and foster the diversification and innovation capabilities of research subjects. Following this, the discussion takes a critical perspective on the challenges presented in the AI-empowered social science research process. Issues such as ethical discrepancies and the dilution of research boundaries are thoroughly examined. In response to these challenges, this paper proposes tailored recommendations to mitigate such concerns effectively. Lastly, this paper anticipates future transformations in social science research prompted by AI, including a shift from explanatory to solution-oriented approaches, from theory-heavy to data-driven frameworks, and from focusing on methodological specifics to embracing overarching methodological principles. The discussion elaborates on the potential mechanisms and pathways to facilitate these transitions, maintaining a strong emphasis on the core principles of social science research. This paper presents a robust argument that AI, while respecting the fundamental essence of social sciences, will catalyze theoretical innovation, methodological refreshment, and enhanced practical applications, thereby offering more scientifically robust solutions to societal challenges.