Convolutional　neural　network　(CNN)　is　currently　one　of　the　most　important　artificial　neural　networks.　However,　traditional　CNN　hardware　architectures　suffer　from　significant　increases　in　energy　consumption　and　processing　time　as　the　demand　for　artificial　intelligence　tasks　grows.　Here,　a　novel　optical　convolution　computing　strategy　is　proposed　that　leverages　a　continuously　adjustable　photoluminescent　device　(CA-PLD)　as　the　optical　convolution　kernel,　enabling　parallel,　all-optical　convolution　computing　and　greatly　simplifying　the　traditional　convolution　process.　Under　ultraviolet　illumination,　the　CA-PLD　exhibits　visible　long-afterglow　emission　characteristics　due　to　the　charge　trapping　and　retention　effects.　This　allows　for　continuously　adjustable　light　weights,　facilitating　arbitrary　convolution　operations.　Building　on　this,　parallel　and　efficient　multiply-accumulate　operations　have　been　successfully　demonstrated　using　CA-PLD　arrays　with　different　weight　combinations.　Moreover,　space-transformable　CA-PLD　units　enable　applications　in　dilated　convolution.　In　a　semantic　segmentation　task　with　20　categories,　the　CA-PLD　units　achieve　higher　Intersection　over　Union　(IoU)　values　and　accuracy.　Therefore,　the　weight-adjustable　and　spatial　transformable　CA-PLD　proposed　in　this　work　holds　promise　for　future　applications　in　intelligent　optical　computing　systems　and　optical　implementations　of　non-von　Neumann　architectures.