It takes significant time and a large amount of data for a traditional convolutional neural network-based target-detection algorithm to train its network parameters. Considering that forest fire data are small samples, this work investigates and implements a forest fire detection algorithm using the transfer learning method to train a convolutional neural network. Experiments on the forest fire dataset in this work show that the detection accuracy of this algorithm can reach 97%. In addition, the algorithm is more adaptable for forest fire detection as it has the advantages of high accuracy, low false alarm rate, and short detection time.