The invention discloses a multi-computing-unit coarse-grained reconfigurable system and method for a recurrent neural network LSTM (long short-term memory). The system comprises multi-matrix-product bias and calculation arrays, activation fitting calculation arrays and vector quantity calculation arrays. The multi-matrix-product bias and calculation arrays are used for realizing calculation and accumulation operation of multiple matrix vector products in the recurrent neural network and perform addition bias calculation under control of control signals, and output values are outputted through corresponding output cache units. The activation fitting calculation arrays are used for realizing a piecewise linear fitting calculation function of activation functions in the recurrent neural network LSTM, activation fitting calculation units are controlled by the control signals to perform corresponding activation function piecewise linear fitting calculation when input values enter input cache units, and output values are outputted through corresponding output cache units. The vector quantity calculation arrays are used for realizing dimension-based vector multiplication and vector addition calculation, and after multiplication units finish calculation, data are transmitted to vector addition units or directly outputted under control of the control signals. Parallelism degree, calculation speed and array utilization rate of the reconfigurable system are increased.