Traditional research on preference reasoning and preference query mainly focuses on the preference of a single object represented by a relational tuple. However， it is a challenge to extend the method of temporal conditional preference query to the extraction sequence of data stream. The problems encountered mainly include the extraction of sequences in data stream and the rapid processing to obtain the dominant sequences and dominant objects. According to the preference data stream， firstly， the Continuous Query Language （CQL） was extended and a special query language named StreamSeq was proposed to deal with the temporal conditional preference on the data stream effectively， which allows the temporal conditional preference specification and reasoning of the sequences extracted from the data stream. Then， an algorithm for extracting object sequences according to temporal index from data stream and an algorithm for performing dominant comparison between sequences were designed， and the dominant sequences satisfying preference condition were returned according to the input data stream. Finally， two sets of data were used for experimental verification. On the synthetic data set， when the number of generated attributes， sequence number， time range and time sliding interval were 10， 8， 20 s and 1 s， the running time acceleration ratio of sequence extraction algorithm and CQL equivalent algorithm was 13.33； on the real data set； when the time range and time sliding interval were 40 s and 1 s， the running time acceleration ratios of the dominant contrast algorithm to mintopK， partition， and incpartition were 10.77， 6.46 and 5.69. Experimental results show that compared with other preference query algorithms， the proposed method needs less running time and is more efficient in getting results.