Application of Data Mining Technology in Enterprise Green Innovation Model Construction and Path Analysis

Area of Science:

• Environmental Science and Technology
• Business and Economics

Background:

• Sustainable development necessitates advancements in green technology.
• Green technological innovation is crucial for achieving sustainability goals.
• Understanding the mechanisms of green technological innovation is vital.

Purpose of the Study:

• To design and optimize an enterprise green technology creation model.
• To leverage data mining techniques for enhancing green technology innovation.
• To analyze the impact of green technology on corporate finance.

Main Methods:

• Focus on data mining technologies, specifically clustering and association rule algorithms.
• Improvement of the traditional clustering algorithm to address data processing and analysis limitations.
• Application of enhanced algorithms for data preprocessing and dynamic incremental clustering.

Main Results:

• The improved clustering algorithm demonstrated a 51.7% enhancement in data processing capabilities.
• The optimized model shows effectiveness in subsequent data preprocessing and clustering tasks.
• The study highlights the positive role of green technology in corporate financial performance.

Conclusions:

• Data mining, particularly improved clustering algorithms, can significantly optimize enterprise green technology creation models.
• Enhanced data processing is critical for effective green technological innovation.
• Green technology adoption positively influences corporate finance, underscoring its strategic importance.