In Taiwan, Crowndaisy Chrysanthemum (CC) is a famous ingredient of chafer and eating chafer is generally known as being related to weather and holiday conditions. This purpose of this paper is to study the feasibility of establishing a daily demand forecast mode of CC with weather data. As for methodology adopting backpropagation. A neural network mode, is introduced to solve this problem because of its admirable learning ability and its successful application in many research fields. An experimentation is used to determine and select out the framework of the neural network for forecasting. The selected framework for forecasting in this paper contains two hidden layers, one neuron in each hidden layer, 0.6 as its learning rate, and 5,000 to be its learning cycles. The convergence RMS value is 0.0888873. After verifying the selected network with several general validate methods of neural network, we conclude that it is feasible to apply neural network to establish a daily demand forecast model of CC with weather data.