The　structured　material　synthesis　route　is　crucial　for　chemists　in　performing　experiments　and　modern　applications　such　as　machine　learning　material　design.　With　the　exponential　growth　of　the　chemical　literature　in　recent　years,　manual　extraction　from　the　published　literature　is　time-consuming　and　labor-intensive.　This　study　focuses　on　developing　an　automated　method　for　extracting　Pd-based　catalyst　synthesis　routes　from　the　chemical　literature.　First,　a　paragraph　classification　model　based　on　regular　expressions　is　employed　to　identify　paragraphs　that　contain　material　synthesis　processes.　The　identified　paragraphs　are　verified　using　machine　learning　techniques.　Second,　natural　language　processing　techniques　are　applied　to　automatically　parse　the　material　synthesis　routes　from　the　identified　paragraphs,　generate　regularized　flowcharts,　and　output　structured　data.　Lastly,　we　utilized　the　structured　data　of　the　synthesis　routes　to　train　machine　learning　models　and　predict　the　performance　of　the　materials.　The　extracted　material　entities　include　the　product,　preparation　method,　precursor,　support,　loading,　synthesis　operation,　and　operation　condition.　This　method　avoids　extensive　manual　data　annotation　and　improves　the　scientific　literature　information　acquisition　efficiency.　The　accuracy　of　the　11　material　entities　exceeds　80%,　and　the　accuracy　of　the　method,　support,　precursor,　drying　time,　and　reduction　time　exceeds　90%.