Chinese Language Feature Analysis Based on Multilayer Self-Organizing Neural Network and Data Mining Techniques.

Area of Science:

• Computational Linguistics
• Artificial Intelligence
• Data Mining

Background:

• Chinese language possesses a rich history and unique characteristics.
• Multilayer self-organizing neural networks (MSONN) and data mining are effective in various prediction tasks.
• Limited application of MSONN and data mining in Chinese language feature analysis.

Purpose of the Study:

• To accurately analyze Chinese language characteristics using MSONN and data mining.
• To evaluate the performance of MSONN for Chinese language feature recognition.
• To compare MSONN with other neural network algorithms for this task.

Main Methods:

• Utilized multilayer self-organizing neural network for feature recognition.
• Employed data mining techniques in conjunction with the neural network.
• Compared the performance of MSONN against alternative neural network algorithms.

Main Results:

• MSONN achieved accuracy, recall, and F1 scores of 68.69%, 80.21%, and 70.19% respectively with large datasets.
• The network maintained high efficiency in feature analysis even under strong noise conditions.
• Demonstrated superior performance of MSONN in Chinese language feature analysis.

Conclusions:

• Multilayer self-organizing neural networks offer a robust approach for Chinese language feature analysis.
• The proposed method provides strong support for understanding and processing the Chinese language.
• MSONN's effectiveness in noisy environments highlights its practical applicability.