Rendongteng（Lonicerae Japonicae Caulis）is the stem and branch of Rendong（Lonicera japonica Thunb.），Huizhanmao Rendong（Lonicera macranthoides），Hongxian Rendong（Lonicera hypoglauca），Huanan Rendong（Lonicera confusa）and Huanghemao Rendong（Lonicera fulvotomentosa）. It contains components such as phenylpropanoids，flavonoids，triterpenoid saponins，iridoids and volatile oil，which have various pharmacological effects such as antioxidant，antibacterial，antiviral，anti-inflammatory and immune regulation effects. It is widely used in clinical practice for rheumatic pain，abscess and other diseases. However，its quality markers are single，lacking characteristic components，and its quality evaluation is not comprehensive. Therefore，this article summarized the research progress on the chemical components，pharmacological effects and clinical applications of Rendongteng（Lonicerae Japonicae Caulis）through literature review，and applied the core concept of quality markers to provide a basis for the quality standards of Rendongteng（Lonicerae Japonicae Caulis）based on different production areas，chemical component measurability and compound compatibility，facilitating its subsequent in-depth research and development and utilization as a reference.

Naoan Dropping Pills（Capsules）［脑安滴丸（胶囊）］is a type B medicine included in medical insurance in China，which is extracted from Chuanxiong（Chuanxiong Rhizoma），Danggui（Angelicae Sinensis Radix），Renshen（Ginseng Radix et Rhizoma），Honghua（Carthami Flos），Bingpian（Borneolum Syntheticum）and other raw materials，and has the effect of activating blood circulation and removing blood stasis，promoting Qi and opening collaterals. Since its introduction to the market nearly 20 years ago，Naoan Dropping Pills has been widely used in clinical practice，and can be used for hemiplegia，hemianopsia，dysarthria，dysarthria caused by cerebral thrombosis，as well as amnesia，dizziness，nausea，photophobia，and fatigue caused by migraine headache，which are symptoms of Qi deficiency and blood stasis，and so on. However，there is currently no guideline or consensus based on evidence to guide the clinical use of Naoan Dropping Pills（Capsules）. With the support of the Chinese Society of Traditional Chinese Medicine（CSTCM），Dongzhimen Hospital of Beijing University of Chinese Medicine（BUCM）led the 24 Chinese units to compile this consensus. The clinical issues to be addressed were identified through a nominal group approach，whereby the drug inserts were systematically reviewed and a clinical study was conducted involving 110 clinicians from across the country. According to the clinical questions，the evidence of clinical studies was systematically retrieved. The GRADE system was used to evaluate the quality of the evidence，and finally，the recommendation/consensus suggestion was reached through the nominal group method. The consensus provides a detailed specification of the medication characteristics，including the timing of medication intervention，dosage，duration of use，precautions，contraindications，and adverse reactions and treatments of Naoan Dropping Pills（Capsules）for the treatment of migraine. This will facilitate the standardisation of the clinical application of this product，improve the clinical efficacy and reduce the risk of medication. At present，this consensus has been reviewed and published by the Standardization Office of the China Association of Chinese Medicine，with a standard number of GS/CACM 359-2024.

The study aims to construct a knowledge graph for traditional Chinese medicine（TCM）gynecology based on large language models. The goal is to systematically organize and present the complex knowledge system of this field，revealing the intrinsic connections and evolution patterns among diseases，thereby enhancing the precision of clinical decision-making. The study selected 349 TCM gynecology papers from China National Knowledge Infrastructure（CNKI）. ChatGLM was used with multi-round prompt templates to achieve zero-shot entity relationship extraction. Evaluation metrics were designed to objectively assess the extraction results. Efficient processing was achieved through API interfaces interacting with the model. The Neo4j graph database was used to construct and visualize the knowledge graph. The study shows that ChatGLM performs better in knowledge extraction compared to traditional Bi-LSTM-CRF models，significantly improving precision，recall and F1 scores. The constructed knowledge graph effectively supports the clinical decision-making in TCM gynecology，enhancing the diagnostic efficiency and accuracy and demonstrating the practical value.

The development of classical meridian theory can be divided into two stages. The early meridian theory of the Western Han Dynasty was based on the anatomical structures and the empirical evidence，with the features of“Yin meridians as arteries and Yang meridians like nerves”. It has a high degree of consistency with modern biomedicine as shown in the 25 pieces of supporting evidence. The meridian theory represented by the handed-down Neijing inherits the early anatomical basic framework and most of the empirical content. While introducing the ancient Confucianism，the holistic views and the virtual concepts，it expands the clinical scope and develops into a practical meridian theory with obvious humanistic medical characteristics. Its main feature is the establishment of the“circulation model of 12 meridians connecting the Zang-Fu organs and the exterior and the interior”，which takes into account the physical and virtual structures and functions as well as the clinical applications. The vitality of the classical meridian theory is rooted in the ancient doctors＇profound insight into the human body structures and the value of clinical application. Reexamining its anatomical basis and scientific connotation will promote the deep integration of traditional medicine and modern medicine and give birth to modern meridian theory.

At the advent of the era of digital intelligence represented by emerging technologies such as big data and artificial intelligence，the digital transformation of all walks of life represented by“artificial intelligence+”has become the general trend. Artificial intelligence enabling traditional Chinese medicine provides a new opportunity for traditional Chinese medicine to upgrade from“fuzzy”diagnosis and treatment mode to“accurate”diagnosis and treatment mode. Focusing on this cross-field，this paper elaborated the application status of artificial intelligence technology in mass data processing，auxiliary diagnosis and disease prediction from the perspective of TCM diagnosis differentiation. At the same time，artificial intelligence technology still faces many challenges in the practical application of traditional Chinese medicine，such as data quality problems caused by the complexity and irregularity of traditional Chinese medicine data，the limited interpretability of artificial intelligence algorithms in traditional Chinese medicine diagnosis，and the difficulties in the integration of traditional Chinese medicine theory and artificial intelligence. This paper discussed these status quo and challenges in depth，which was of great significance for promoting the further development and application of artificial intelligence in digital diagnosis of traditional Chinese medicine.

With the rise and rapid development of artificial intelligence，deep learning，as an important part of the field of artificial intelligence，has achieved significant results in various fields such as materials science，biomedicine，literature，and art，demonstrating high versatility and value. As an emerging deep learning method，Graph Neural Network（GNN）overcomes the limitation of traditional deep learning that can only be applied to Euclidean data frameworks，demonstrating its powerful ability to handle non-Euclidean complex data especially graph data，and demonstrating its enormous potential. Currently，GNN has been widely applied in the medical field and has been preliminarily applied in clinical and basic researches of traditional Chinese medicine. They have achieved certain results in traditional Chinese medicine prescription recommendation，symptom diagnosis and target prediction of traditional Chinese medicine. By reviewing the current application status of GNN in the field of traditional Chinese medicine and combining the characteristics of current GNN technology，it looks forward to the future development of this technology in the field of traditional Chinese medicine. It is believed that GNN has broad application prospects in clinical diagnosis，prescription treatment，traditional Chinese medicine research and utilization of classics. It is to provide ideas and inspiration for future research on Traditional Chinese Medicine based on graph neural networks so that traditional Chinese medicine can keep pace with the times，enhance intelligence and objectivity，and better serve the people's medical and health care and health cause of the motherland.