This paper studies the methods to improve end-to-end neural coreference resolution.First,we introduce a coreference cluster modification algorithm,which can help modify the coreference cluster to rule out the dissimilar mention in the cluster and reduce errors caused by the global inconsistence of coreference clusters.Additionally,we tune the model from two aspects to get more accurate coreference resolution results.On one hand,the simple scoring function is replaced with a feedforward neural network when computing the head word scores for later attention mechanism which can help pick out the most important word.On the other hand,the maximum width of a mention is tuned.Our experimental results show that above methods improve the performance of coreference resolution effectively.