The existing knowledge concept recommendation system does not consider the short-term interest of users. To solve the problem， a Knowledge Concept Recommendation system based on Interest Enhancement （KCRec-IE） was proposed. Firstly， users’ short-term interests were captured according to the users’ knowledge concept click sequences， and a heterogeneous graph was constructed by using the side information. Then， the representation learning of knowledge concept entities and user entities was carried out on heterogeneous graph by using meta-path-guided graph convolution. Different from the representation learning of knowledge concept entities， when learning the representation of user entities， the contributions of different neighbor users to target users were able to be distinguished according to the short-term interests of users. Finally， the score prediction was realized according to the knowledge concept entities， the user entities and the user’s short-term interests. Experimental results on public dataset Xuetang X show that compared with KCRec-SEIGNN， KCRec-IE is improved by 3.60 percentage points on HR@5； compared with KCRec-IE_n， KCRec-IE is improved by 1.02 percentage points on HR@10； compared with KCRec-SEIGNN， KCRec-IE is improved by 1.60 and 1.18 percentage points respectively on NDGC@5 and NDGC@10 respectively， verifying the effectiveness of the proposed method.

Considering that the microphone array is mostly used as a sound pickup device in the smart speaker,and there are distortion and complexity of the acoustic echo cancellation in the adaptive filtering technology on single channel,a fast echo cancellation algorithm for microphone array was proposed. First of all,the adaptive filtering technology was used to estimate the first channel echo,then estimate the relative echo transfer function between the arrays,and the echoes of other channels were obtained by multiplying the above two. Secondly,the estimated echo and the noise were regarded as the noise reference signals of the Generalized Sidelobe Canceller(GSC)beamforming lower branch,which were removed by the GSC beamforming algorithm. The simulation results show that the proposed algorithm has good echo cancellation and noise suppression performance under moderate reverberation,long distance,low echo to noise ratio and using music as echo environment. And the algorithm not only has small computational complexity,but also makes target speech signals have high signal distortion ratio and intelligibility.

Under the cloud computing environment, it has become a significant problem to decrease the power consumption while the makespan is shortened in the process of scheduling resource. Thus, this paper made span and power consumption as the optimization objectives and established power-aware resource scheduling model, then improved the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) by adopting special initialization and the learning algorithm, to solve the problem of power-aware scheduling. Consequently, the simulation results prove that the proposed scheduling algorithm not only shortens the makespan, but also decreases the power consumption effectively.