The research on word game based on SIRS-ARIMA model and machine learning algorithm

Abstract

Abstract Various word game software is becoming more and more popular, such as the recently popular “Wordle” crossword game, which can entertain, develop intelligence, and improve word learning ability. However, there are little research on how to improve the challenge and innovation of word games. For this challenge, this paper focuses on the research of word games based on SIRS-ARIMA model and machine learning algorithm. The SIRS-ARIMA model is an innovative approach that combines the SIRS propagation model and the autoregressive integrated moving average model (ARIMA) to analyze and predict dynamic changes in the word game by taking into account factors such as social media propagation. This paper also uses the entropy method of machine learning algorithm and SVC model to classify the difficulty of words, so as to optimize the design and play of word games. By analyzing player behavior and word attributes, it can personalize the game experience and provide players with precise feedback mechanisms. This research provides new theories and methods for the development of word games and provides strong support for the design of more engaging and innovative games.