To　solve　the　problem　of　uncertainty　of　initialization　methods　and　insufficient　amount　of　labeled　data,　an　initialization　method,　which　used　unlabeled　data　for　principal　component　initialization　of　network　parameters　and　included　four　steps,　i.e.,　sampling,　principal　component　computation,　initializing　and　rearranging　the　convolutional　kernels,　was　proposed　in　this　paper.　First,　the　feature　map　was　sampled　with　moving　the　reception　fields,　then　the　image　blocks　corresponding　to　all　the　receptive　fields　of　the　feature　map　were　obtained　and　a　sampling　set　was　formed.　Second,　the　principal　components　of　the　sampling　set　were　computed.　Finally,　the　principal　components　were　used　to　initialize　the　network　parameters　and　the　convolutional　kernels　were　rearranged　to　improve　recognition　performance.　In　cases　with　the　same　network　architecture　and　datasets,　i.e.,　STL-10　and　CIFAR-10　datasets,　an　increase　of　recognition　accuracy　of　4%-20%　was　obtained　by　this　method　in　comparison　with　the　traditional　initialization　methods.　The　results　indicate　that　the　proposed　method　can　make　full　use　of　unlabeled　data　for　initializing　network　parameters　to　obtain　remarkable　recognition　effectiveness.　Moreover,　through　the　performance　evaluation　of　the　algorithm,　it　proves　that　the　method　is　obviously　better　than　the　traditional　initialization　methods.　©　2019,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.