To address the problem of lack of temporal correlations and spatial location relationships in imaging time series， Fusion-Imaing Recurrent Capsule Neural Network （FIR-Capsnet） for time series was proposed to fuse and extract spatial-temporal information from time series images. Firstly， the multi-level spatial-temporal features of time series images were captured by using Gramian Angular Field （GAF）， Markov Transition Field （MTF） and Recurrence Plot （RP）. Then， the spatial relationships of time series images were learnt by the rotation invariance of capsule neural network and iterative routing algorithm. Finally， the temporal correlations hidden in the time series data were learnt by the gate mechanism of Long-Short Term Memory （LSTM） network. Experimental results show that FIR-Capsnet achieves 15 wins on 30 UCR public datasets and outperforms Fusion-CNN by 7.2 percentage points in classification accuracy on Human Activity Recognition （HAR） dataset， illustrating the advantages of FIR-Capsnet in processing time series data.