This　paper　presents　an　in-depth　study　and　analysis　of　the　model　of　English　writing　using　artificial　intelligence　algorithms　of　neural　networks.　Based　on　word　vectors,　the　unsupervised　disambiguation,　and　clustering　of　multimedia　contexts　extracted　from　massive　online　videos,　the　disambiguation　accuracy　reaches　over　0.7,　and　the　resulting　small-scale　multimedia　context　set　can　cover　up　to　90%　of　vocabulary　learning　tasks;　user　experiments　show　that　the　multimedia　context　learning　system　based　on　this　method　can　improve　the　effectiveness　and　experience　of　ESL　vocabulary　learning,　as　well　as　the　long-term　word　sense　memory　of　learners.　The　results　are　30%　better.　Based　on　the　dependency　grammatical　relations　and　semantic　metrics　of　collocations　on　a　large-scale　professional　corpus,　we　established　a　collocation　intention　description　and　retrieval　method　in　line　with　users＇　linguistic　cognition　and　doubled　the　usage　rate　of　collocation　retrieval　on　the　actual　deployment　system　after　half　a　year,　becoming　a　user　＂sticky＂　ESL　writing　aid,　and　further　defined　style.　Dictionaries　only　provide　basic　lexical　definitions,　and,　even　if　supported　by　example　sentences,　they　still　cannot　meet　the　needs　of　ESL　authors　in　terms　of　expressive　accuracy　and　richness.　However,　the　current　machine　translation　is　based　on　the　black　box　deep　neural　network　construction,　and　its　translation　process　is　not　understandable　and　interactive.　Among　the　three　algorithmic　models　constructed　in　this　paper,　the　multitask　learning　model　outperforms　the　conditional　random　field　model　and　the　LSTM-CRF　model　because　the　multitask　learning　model　with　auxiliary　tasks　solves　the　problem　of　sparse　data　to　a　certain　extent,　allowing　the　model　to　be　trained　more　adequately　in　the　case　of　uneven　label　distribution,　and　thus　performs　better　than　other　models　in　the　task　of　grammatical　error　detection.