For the fact that institutional trading in the financial market is highly misleading to retail investors in the financial market, a trend prediction method based on the impact of institutional trading behaviors was proposed. First, using the time series Matrix Profile (MP) algorithm and taking the stock turnover rate as the cut-in point, a knowledge base of turnover rate fluctuations based on the influence of institutional trading behaviors under motifs with different lengths was constructed. Second, the motif's length, which leads to the high accuracy of the prediction result of the stock to be predicted was determined. Finally, the fluctuation trend of single stock under the influence of institutional trading behaviors was predicted through the knowledge base of this motif's length. In order to verify the feasibility and accuracy of the new method of trend prediction, the method was compared with Auto-Regressive Moving Average (ARMA) model and Long Short Term Memory (LSTM) network, and the Root-Mean-Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) evaluation indicators were used to compare the 70 stocks' prediction results of three methods. The analysis of experimental results show that, compared with the ARMA model and the LSTM network, in the prediction of 70 stock price trends, the proposed method has more than 80% of the stock prediction results more accurate.