The study of research trends is pivotal for understanding scientific development on specific topics. Traditionally, this involves keyword analysis within scholarly literature, yet comprehensive tools for such analysis are scarce, especially those capable of parsing large datasets with precision. pyKCN, a Python toolkit, addresses this gap by automating keyword cleaning, extraction and trend analysis from extensive academic corpora. It is equipped with modules for text processing, deduplication, extraction, and advanced keyword co-occurrence and analysis, providing a granular view of research trends. This toolkit stands out by enabling researchers to visualize keyword relationships, thereby identifying seminal works and emerging trends. Its application spans diverse domains, enhancing scholars' capacity to understand developments within their fields. The implications of using pyKCN are significant. It offers an empirical basis for predicting research trends, which can inform funding directions, policy-making, and academic curricula. The code source and details can be found on: https://github.com/zhenyuanlu/pyKCN

翻译：研究趋势分析对于理解特定主题下的科学发展至关重要。传统上，这涉及对学术文献中的关键词进行分析，然而，能够精确解析大型数据集的综合分析工具却十分稀缺。pyKCN 是一个Python工具包，通过自动化的关键词清洗、提取及趋势分析，填补了这一空白，可应用于大规模学术语料库。该工具包配备文本处理、去重、提取以及高级关键词共现与分析模块，能够提供研究趋势的细致视角。其突出之处在于，研究者可以可视化关键词之间的关系，从而识别出奠基性成果与新兴趋势。该工具的应用横跨多个领域，增强了学者把握自身领域发展的能力。使用pyKCN的意义重大：它为预测研究趋势提供了实证基础，可指导资助方向、政策制定及学术课程设置。代码来源及详情见：https://github.com/zhenyuanlu/pyKCN