Emphasizing symptom efficacy is an important manifestation of the personalized diagnosis and treatment of traditional Chinese medicine （TCM）. However， in current clinical practice of TCM， there are challenges such as diverse symptom expressions， difficulty in standardization， inconsistent evaluation standards for symptom efficacy， lack of universal quantitative methods， and complexity in collecting complete and accurate symptom information. These issues hinder the full and effective utilization of symptom information. Addressing the current research status and existing problems of symptom terminology standardization， quantification and grading of symptom efficacy， and collection of symptom information， this paper proposes methodological strategies for effectively recording and utilizing TCM symptom efficacy information in the era of big data. These strategies include collecting TCM symptom information based on patient reporting， standardizing the evaluation of TCM symptom efficacy from measurement scales and evaluation dimensions， integrating TCM symptom efficacy evaluation into clinical diagnosis and treatment processes， and utilizing artificial intelligence technology to acquire and process TCM symptom efficacy information. TCM symptom efficacy evaluation strategies based on patient perspectives and artificial intelligence technology will help fully explore the value of data elements， promote the objective demonstration of the specific efficacy of TCM， and facilitate the discovery of effective patterns.