  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
