Against the backdrop of the accelerating global energy transition toward clean and low-carbon sources, hydrogen energy is emerging as a vital component of future energy systems due to its zero-carbon emissions, high energy density, and renewable nature. The safe and efficient transportation of hydrogen is a critical link in the hydrogen energy industry chain. As core connecting components in hydrogen transmission systems, the sealing integrity, hydrogen embrittlement resistance, and long-term service reliability of hydrogen pipeline joints directly impact the stable operation of entire hydrogen transmission systems and the feasibility of large-scale application. This study systematically reviews the research literature on hydrogen pipeline joints from 2014 to 2025 using bibliometric and knowledge graph analysis methods based on the Web of Science Core Collection database. It constructs co-occurrence networks and clustering graphs of keywords to identify core research themes in this field, including hydrogen embrittlement failure mechanisms, degradation of sealing material properties, structural design optimization of joints, and intelligent monitoring and fault diagnosis. Furthermore, this study highlights existing research gaps in evaluating joints’ long-term service performance, developing low-cost and efficient manufacturing technologies, and verifying reliability under complex operating conditions. This study provides a systematic bibliometric perspective on hydrogen pipeline joint technology development, aiding in identifying research frontiers and technological evolution pathways. It offers theoretical support and decision-making references for the safe construction and standardized development of hydrogen energy infrastructure.

Intelligent oilfield development is experiencing an increasingly deep integration between localized automation and integrated, data-centric ecosystems. To systematically delineate the knowledge structure and technological trajectories within this field, this study analyzes 225 high-quality publications. This study innovatively employs a custom toolchain based on the Dart language for heterogeneous data cleaning and standardization, ensuring high accuracy and scientific rigor in the analysis samples. The investigation reveals a distinct dual-core driving mechanism underpinning recent advancements: a cognitive cluster centered on Artificial Intelligence and Deep Learning for complex data interpretation and prediction, and a decision-making cluster focused on Operational Optimization and Predictive Modeling for production enhancement. These two clusters respectively encompass eight sub-clusters: “artificial intelligence,” “machine learning,” “deep learning,” “performance,” “enhanced oil recovery,” “model,” “optimization,” and “predication.” This dual-core framework signifies a paradigm shift from experience-based practices to a synergistic “AI-enabled + mathematical optimization” approach. The analysis further explores emerging trends, including the potential of deep reinforcement learning for dynamic decision-making and the critical role of cybersecurity and model robustness in safety risk management. By mapping the current landscape and core mechanisms, this study provides a foundational reference for researchers and practitioners to navigate the future development of intelligent oilfields towards more resilient and efficient ecosystems.