Dangerous driving behaviors are diverse and complex. Determining how to analyze the driving behavior of public drivers objectively and accurately has always been a research challenge. This research proposes a macroscopic and dynamic method for evaluating drivers' dangerous driving degree based on a fuzzy inference system. It also designs fuzzy-macro long short-term memory (LSTM), a variant of LSTM recurrent neural networks, which can predict drivers' dangerous driving behaviors and risk degree. We elucidate how a macroscopic fuzzy inference dangerous driving behavior system is designed based on various driving behavior factors and the neuron architecture of the fuzzy-macro LSTM network. We collect real driving behavior data of drivers on the road and conduct a series of experimental analyses. Compared with five other commonly used time-series forecasting neural network models, our fuzzy-macro LSTM model performs best in terms of prediction error. Experimental results verify the effectiveness of the proposed method for macroanalysis and prediction of dangerous driving behavior.