To　initialize　convolutional　neural　networks　better,　an　effective　method　named　principal　component　analysis　(PCA)　Shuffling　initialization　was　proposed.　The　method　consisted　of　three　steps.　First,　for　the　first　convolutional　layer,　all　receptive　field　of　each　feature　map　on　training　set　was　sampled.　Then,　principal　component　analysis　of　image　patches　separately　for　each　feature　map　was　conducted,　and　projection　matrix　was　used　to　initialize　filter　of　first　convolutional　layer.　Finally,　the　first　two　steps　on　the　other　convolutional　layers　layer-wisely　were　performed.　Experimental　results　on　MNIST　and　CIFAR-10　dataset　show　that　the　proposed　initialization　has　advantages　of　accuracy　and　speed　of　convergence　compared　to　the　common　method　such　as　random　initialization　and　Xavier　initialization.　©　2017,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.