Cultural ecosystem services (CES) are widely measured but rarely attributed to the specific landscape elements that generate them. We coupled element-level image recognition with service-level text analysis across 20,447 reviews and 18,997 photographs from three social media platforms covering 15 national-, provincial-, and municipal-level forest parks in Guangzhou, China (2020–2024). A 2660-term dictionary quantified nine CES; a fine-tuned deep learning classifier identified 28 landscape elements; multiple correspondence analysis described the element-service structure and ridge regression tested associations within four case parks. Natural elements aligned with ecological, aesthetic, and inspirational services and built elements with cultural, social, and wellness services, but two results qualify that division. First, individual elements were associated with different services in opposite directions: seasonal forest with higher aesthetic appreciation but lower recreation, water features with higher aesthetic value but lower inspiration. Second, the mapping differed across the four case parks rather than holding constant; in the municipal case park, only built facilities positively predicted social interaction. These four parks are illustrative exemplars rather than representative samples of their tiers. Dynamic biological elements (birds 0.43%, insects 0.12%, fish 0.08%) ranked lowest of all 28 elements. These are descriptive associations awaiting mechanistic explanation; on that reading, planning for CES would be better organized around intended services than element inventories, calibrated to the individual park.