The algorithm platform， as the implementation way of automatic machine learning， has attracted the wide attention in recent years. However， the business processes of these platforms need to be built manually， and these platforms are faced with inflexible model calling and the incapability of customized automatic algorithm construction for specific business requirements. To address these problems， an algorithm path self-assembling model for business requirements was proposed. Firstly， the sequence features and structural features of code were modeled simultaneously based on Graph Convolutional Network （GCN） and word2vec representation. Secondly， functions in the algorithm set were further discovered through a clustering model， and the obtained function subsets were used for the preparation of the path discovery of algorithm components between subsets. Finally， based on the relationship discovery model and ranking model trained with prior knowledge， the self-assembled paths of candidate code components were mined， thus realizing the algorithm code self-assembling. Using the proposed evaluation indicators for comparison and analysis， the best result of the proposed algorithm path self-assembling model is 0.8， while that of the baseline model Okapi BM25+word2vec is 0.21. To a certain extent， the proposed model solves the problem of missing code structure and semantic information in traditional code representation methods and lays the foundation for the research of refinement of algorithm process self-assembling and automatic construction of algorithm pipelines.