Ocular images play an essential role in ophthalmology. Current research mainly focus on computer-aided diagnosis using slit-lamp images, however few studies have been done to predict the progression of ophthalmic disease. Therefore by exploring an effective approach of prediction can help to plan treatment strategies and to provide early warning for the patients. In this study, we present an end-to-end\u00a0temporal sequence network (TempSeq-Net) to automatically predict the progression of ophthalmic diseases based on consecutive slit-lamp images. First, we comprehensively compare six potential combinations of three convolutional neural networks and long short term memory (or recurrent neural network) in terms of effectiveness and efficiency, to obtain the optimal TempSeq-Net model. Second, we analyze the impacts of sequence lengths on model\u2019s performance which help to evaluate their stability and validity and to determine the appropriate range of sequence lengths.