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To meet both the precision and convergence rate requirement of reconstruction algorithm, an improved compressed sensing reconstruction algorithm based on artificial neural network (IANN-CS) is proposed in this paper. The approach applies Artificial Neural Network structure to compressed sensing (ANN-CS) to reconstruct sparse signal, and on this basis, a dynamic learning factor is obtained by using gradient descent method repeatedly to replace the one which is a constant in ANN-CS algorithm, this improved algorithm is also called IANN-CS in this paper. The experimental results show that, compared with ANN-CS algorithm, IANN-CS algorithm has greatly improved convergence rate with a little change in convergence precision. In addition, under the same reconstruction conditions, IANN-CS algorithm has a good compromise between reconstruction precision and convergence rate, what is more, the observation value needed in ANN CS and IANN-CS algorithm are less than which in the existing reconstruction algorithms.