This paper presents a novel set of image features that encode the local, color, spatial, relative intensity information and gradient orientation of the painting image for painting artist classification, style classification as well as artist and style influence analysis. In particular, a new color DAISY Fisher vector (CD-FV) feature is first created by computing Fisher vectors on densely sampled DAISY features. Second, a color WLD-SIFT Fisher vector (CWS-FV) feature is developed by fusing Weber local descriptors (WLD) with Scale Invariant Feature Transform (SIFT) descriptors and Fisher vectors are computed on the fused WLD-SIFT features. Finally, an innovative color multi-fusion Fisher vector (CMFFV) feature is developed by integrating the Principal Component Analysis (PCA) features of CD-FV, CWS-FV and color SIFT-FV features. The effectiveness of the proposed CMFFV feature is assessed on the challenging Painting-91 dataset. Experimental results show that the proposed CMFFV feature is able to (i) achieve the state-of-the-art performance for painting artist classification, (ii) outperform other popular image descriptors, as well as (iii) discover the artist and style influence to understand their connections and evolution in different art movement periods.