An interactive retrieval computer implemented method performed on an onboard computer of an electronic flight bag, said electronic flight bag further including an electronic database stored in the computer, the method comprising: S1: extending a query statement input by a user, namely a pilot of an aircraft comprising the electronic flight bag, in combination with a knowledge graph, in order to obtain an extended query statement; wherein S1 comprises: S101: searching the knowledge graph for a plurality of nodes corresponding to the query statement; S102: searching for a closest node to the nodes corresponding to the query statement; and S103: adding a specialized word corresponding to the closest node, into the query statement, so as to create the extended query statement; S2: selecting, from a document library, a plurality of documents having a highest semantic similarity with the extended query statement, wherein the semantic similarity is calculated from average dense word vectors of a word set, and then returning the documents to the user; and S3: adjusting a weight of each node in the knowledge graph in accordance with user behaviors, so as to optimize a retrieval result; wherein the user behaviors including the following information records: the start time of entering of the query statement by the user, the content of the query statement, the time at which document summary is clicked, the sequential position of the clicked document summary, and the document where the clicked document summary is located, which are monitored via a monitoring mechanism and recorded as a pilot query log; the weights between the nodes are adjusted according to the pilot query log, wherein the adjusted weight is r 12 ′ = α + 1 − α ⋅ . r 12 ∈ ( r 12 ,1), wherein an associated weight of the query statement and a pilot-selected text is set to be r 12 ,α is a reward factor and (0<α<1). S1: extending a query statement input by a user, namely a pilot of an aircraft comprising the electronic flight bag, in combination with a knowledge graph, in order to obtain an extended query statement; wherein S1 comprises: S101: searching the knowledge graph for a plurality of nodes corresponding to the query statement; S102: searching for a closest node to the nodes corresponding to the query statement; and S103: adding a specialized word corresponding to the closest node, into the query statement, so as to create the extended query statement; S101: searching the knowledge graph for a plurality of nodes corresponding to the query statement; S102: searching for a closest node to the nodes corresponding to the query statement; and S103: adding a specialized word corresponding to the closest node, into the query statement, so as to create the extended query statement; S2: selecting, from a document library, a plurality of documents having a highest semantic similarity with the extended query statement, wherein the semantic similarity is calculated from average dense word vectors of a word set, and then returning the documents to the user; and S3: adjusting a weight of each node in the knowledge graph in accordance with user behaviors, so as to optimize a retrieval result; wherein the user behaviors including the following information records: the start time of entering of the query statement by the user, the content of the query statement, the time at which document summary is clicked, the sequential position of the clicked document summary, and the document where the clicked document summary is located, which are monitored via a monitoring mechanism and recorded as a pilot query log; the weights between the nodes are adjusted according to the pilot query log, wherein the adjusted weight is r 12 ′ = α + 1 − α ⋅ . r 12 ∈ ( r 12 ,1), wherein an associated weight of the query statement and a pilot-selected text is set to be r 12 ,α is a reward factor and (0<α<1). <EOS>