Wordle,　a　novel　word　puzzle　courtesy　of　the　New　York　Times,　is　becoming　popular　on　the　Internet.　Since　players　are　willing　to　share　their　scores　on　Twitter,　this　adds　a　social　attribute　to　the　game.　We　first　conducted　a　multi-factor　ANOVA　on　the　attributes　of　the　words　and　the　percentage　of　scores　in　the　difficult　mode.　The　results　showed　that　the　letters　in　the　first　four　positions　of　the　word　and　the　number　of　repeated　letters　in　the　word　had　an　effect　on　the　percentage　of　scores.　Next,　we　proposed　a　multiple-input-multiple-output　long　and　short-term　memory　(LSTM)　model　to　predict　the　distribution　of　reported　outcomes.　To　incorporate　the　internal　effects　of　the　data　into　the　study,　we　designed　a　six-input-seven-output　LSTM　regression　prediction　model　and　trained　it　with　the　dataset.　Then,　the　trained　network　was　used　to　predict　the　distribution　of　＇EERIE＇scores.　To　predict　the　difficulty　of　words,　a　random　forest　model　was　designed　to　determine　which　of　these　attributes　were　associated　with　each　classification　by　using　several　attributes　carefully　selected　from　the　words.　Finally,　the　difficulty　of　the　word　＇EERIE＇was　predicted.　©　2023　IEEE.