Abstract:  The fresh cut flower price index is a trend indicator reflecting the current status of the fresh cut flower market. It is of great significance to study the change of fresh cut flower price index and grasp the dynamics and regularity of flower market. Aiming at the sequence characteristics of fresh cut flower price index, this paper constructs the cut flower price index short-term forecasting model based on the L-M optimization algorithm of BP model. The model uses tansig and purelin as the transfer function between layers, uses the time series analysis method to determine the number of input layer neurons, and by comparison with experimental data to determine the number of hidden layer neurons. Three evaluation indexes of mean absolute error, mean relative error and root mean square error are used to test the prediction accuracy of the model. The experimental results show that the model is effective and has practical application value.

Key words:  fresh cut flower price index, BP model, short-term prediction, evaluation indexes