As living standards improve, the demand for artworks has been escalating, transcending beyond the realm of mere basic human necessities. However, amidst an extensive array of artwork choices, users often struggle to swiftly and accurately identify their preferred piece. In such scenarios, a recommendation system can be invaluable, assisting users in promptly pinpointing the desired artworks for better service design. Despite the escalating demand for artwork recommendation systems, current research fails to adequately meet these needs. Predominantly, existing artwork recommendation methodologies tend to disregard users’ implicit interests, thereby overestimating their capability to articulate their preferences in full and often neglecting the nuances of their diverse interests. In response to these challenges, we have developed a weighted artwork correlation graph and put forth an embedding-based keyword-driven artwork search and recommendation methodology. Our approach transforms the keywords that delineate user interests into word embedding vectors. This allows for an effective distinction between the user’s core and peripheral interests. Subsequently, we employ a dynamic programming algorithm to extract artworks from the correlation graph, thereby obtaining artworks that align with the user’s explicit keywords and implicit interests. We have conducted an array of experiments using real-world datasets to validate our approach. The results attest to the superiority of our method in terms of its efficacy in searching and recommending artworks.