Wordle game analysis based on Grey Wolf optimization algorithm and LSTM

Wordle is a popular crossword game that allows players to fill in six words, five letters at a time. In order to explore the reasons for the popularity of Wordle games, this paper aims to use machine learning to analyze and study the data of Wordle games. First, the LSTM algorithm is used to predict the number of Wordle players and analyze the changes in the number of players. Then a more accurate model is built to predict the probability distribution of 1 to 6 correct guesses for each word in Wordle game, and a multi-input and multi-output prediction model is built based on the bionic encircle tracking method of Grey Wolf optimization algorithm (GWO). The above machine learning algorithm is used to systematically predict and study the future development trend and mathematical connotation of Wordle games.