With the birth of the Internet, information and data can flow in both directions, and, after that, it has spread to thousands of households. On this basis, e-commerce was also born, and it took a very short time from the field that no one cared about at the beginning to the hot focus field now. The development of e-commerce has broadened the sales channels of farmers, which has further increased the sales benefits of fruits and other agricultural products, conformed to the development of the times, and further supplemented the sales of fruits and other agricultural products. Based on the use of embedded microprocessors, this paper constructs an e-commerce system integrating sales and logistics. Based on the logistics model in the e-commerce platform, an e-commerce logistics identification model is constructed to measure the e-commerce logistics service level of agricultural products such as fruits. The article analyzes the sales and transportation methods of fruits and other agricultural products. Through a detailed analysis of the traditional e-commerce system, the e-commerce sales system has been optimized and improved by combining the reference cases and the data obtained from the Internet, especially in the aspect of logistics transportation. According to the data experiments in this article, it is shown that, after using the data formula for calculation, the best plan for transporting fruits and other agricultural products can be obtained. In the calculation result of the formula, the cost of delivery without considering customer satisfaction is 586 yuan, and the average delivery time of the vehicle is 5.625 hours, but the average satisfaction is only 78.5%. Compared with customer satisfaction, although the cost is about 15 yuan more and the delivery time is a few minutes longer, the customer satisfaction is about 21% higher than the customer satisfaction without considering the customer. Based on the optimized and transformed e-commerce system, this article will further improve the e-commerce platform for fruits and other agricultural products.

Smart tourism recommendation refers to supplementing suggestions for tourists’ self-service sightseeing plans based on Internet technology, and recommending more effective and practical tourism information. This paper uses big data and artificial intelligence algorithms to study the smart tourism recommendation method in Southeast Asia. First of all, this paper gives a brief overview of the definition and classification of big data and artificial intelligence algorithms and then introduces tourism resources and smart tourism in Southeast Asia. Finally, this paper conducts a comparative experiment between the smart tourism development model based on big data and artificial intelligence algorithms and the traditional tourism development model and analyzes the utility of the development model from three aspects, namely, tourists’ sense of experience and satisfaction, the scale of tourism transactions and the growth rate of tourism revenue, and the adequacy and harmony of tourism resource allocation. The final experimental results show that the overall average value of tourists’ experience and satisfaction under the smart tourism recommendation mode based on big data and artificial intelligence algorithms is 88.84 points, which is 7.30 points higher than the traditional tourism mode, which verifies its effectiveness.

Currently, the 1st generation and 2nd generation solar water heating tanks’ design is unsatisfactory in the current use situation. When hot water is used with the hot water being consumed, thus cold water will be added into the same tank at the same time, resulting in a significant drop in the temperature of the hot water in the tank and making it necessary to use an additional electric heating rod for reheating. To address this shortcoming, a 3rd generation solar water heating tank was designed to improve the situation. In other words, a “movable dividing disk” is added inside the tank to completely isolate the hot water from the cold water so that the entire tank of hot water can be 100% utilized. However, when the hot water is exhausted, the 3rd generation solar water heating tank must adopt an electric heating rod to reheat the entire tank of water or make an additional small heating tank. To solve this problem, this article develops the 4th generation intelligent energy-saving solar water heating tank, in which a “fixed dividing disk” is installed inside the water heating tank to create a cold-water heating zone of about 10% of the total capacity for local heating. This allows for an efficient and faster hot water supply, saving about 90% of energy when the first tank of hot water is used up and a separate electric heating rod is used for heating.

The arrival of the era of big data has realized the transformation of peoples production and lifestyle. At the same time, it also increases peoples desire to consume, and the feedback behavior of consumers comments and ratings is the feedback of users experience in merchants products, that is, the matching of products to consumer needs and preferences. When the product can reach the users satisfaction level, the customer-aware mobile terminal system is constructed and optimized by using the advanced methods and technologies of big data information display and the principles and laws of the collaborative filtering algorithm in cloud computing. It ensures the ecological development of the consumer industry. Among them, in the experimental evaluation of the collaborative filtering recommendation algorithm, the mean absolute error (MAE) and root mean square error (RMSE) values of the SVD⁺⁺ algorithm are higher than those of the other three algorithm models, indicating that other algorithm models can effectively improve the accuracy of the recommendation algorithm. A cross-sectional comparative analysis of experimental results has shown that, as the number of neighbors increased, the MAE and RMSE values first decreased and then increased. When the number of neighbors N is 25, the MAE and RMSE reach the minimum value, so the optimal number of neighbors is 25. Therefore, it is very important to use the collaborative filtering algorithm to analyze and construct the consumer behavior and customer perception mobile terminal system.

Lantern Festival is the most important cultural event in Chinese ethnic society. With a tremendous economic and tourism value brought by the event activities, Taiwan cities are actively participating the bidding to host Taiwan Lantern Festival. Event site selection is arguably a complex issue because of a multitude of variables that are needed to be taken into consideration. Therefore, Delphi method and analytical hierarchy process (AHP) are used to develop an evaluation hierarchy for Taiwan Lantern Festival site selection. The proposed model not only provides the site selection criteria but also determines their relative importance of different criteria in influencing site selection decisions. Results show that tourism attraction and visitors' needs are the most important factors for selecting Taiwan Lantern Festival site and other important factors according to the rank of their weights are site environment, accessibility, and local support. The factors identified have implications for management practice, for site decision making by Taiwan Visitor Bureau and event organizers.