The invention relates to an electric energy quality prediction method based on similar days and improved LSTM. The method comprises the following steps: 1) collecting electric energy quality steady-state index data of a certain monitoring point in a certain region within a period of time and meteorological data of the region; 2) performing feature dimension reduction on the meteorological data byadopting a kernel principal component analysis method to obtain similar day feature vectors; 3) calculating meteorological factor matching coefficients of the historical day and the to-be-predicted day by adopting a grey correlation algorithm, and determining a similar day set; 4) selecting power quality historical data similar to the day to be predicted and similar day set data as a training sample set of the LSTM neural network, and optimizing LSTM neural network parameters by adopting a method of combining a Dropout algorithm and an Adam adaptive learning rate optimization algorithm, and taking the similar day feature vector as a model input variable to obtain a prediction result of the electric energy quality of each moment of the to-be-predicted day. Compared with the prior art, the method has the advantages of avoiding training overfitting, avoiding information interference and information repetition, improving prediction accuracy and the like.