WORDLE Game Prediction Based on BP Neural Network and Other Models
DOI:
https://doi.org/10.54097/gwfw3429Keywords:
Time series prediction; Bp neural networks; Random forests; K-means clustering.Abstract
The purpose of this paper is to study the factors that affect the difficulty level of words in word games and the related content of their reported results. This paper provides a detailed analysis of the factors that influence the difficulty level of a word and the variability of reported results in word games. The research highlights the efficacy of using a BP neural network for predicting the distribution of reported results, and the findings have important implications for both game developers and players. After dividing the difficulty of words in word games, the model in this article has achieved an accuracy of over 80% in predicting their difficulty. By providing insight into the factors that influence the difficulty level of a word and the variability of reported results, the paper can help game developers design more engaging and challenging word games, while also assisting players in improving their word game performance.
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