For stock investors, the Jinlin Analyst Research Report offers authoritative, professional, timely and comprehensive insights to help uncover potential thematic opportunities. The National Day long holiday has always been a key window for domestic annual consumption. This year, more and more travelers are no longer manually scrolling through social platforms and comparing guides, but instead directly input their travel budget, destination and taste preferences into large models and AI agents to generate a complete holiday plan with one click. AI is becoming the "travel buddy" of more and more people.

This change is supported by data. From the summer of 2026 to National Day, the popularity of AI travel tools continued to rise. Fliggy data shows that during the summer, tourism-related AI interactions on the platform increased by 297% quarter on quarter; DeepTrip, the travel agent under Tongcheng Travel, saw hourly visits during peak periods on the first day of National Day increase by more than 3 times. Users can input travel time, destination, number of people, budget and travel preferences, and then generate daily itineraries, attraction suggestions and accommodation recommendations, and can also continue to request modifications, adjust the order and add or remove attractions. A relevant person in charge of Tongcheng Travel's R&D and Cloud Empowerment Center previously said publicly that the core value of AI in travel planning should not only remain at the output of text plans, but also solve the real problem of "what to do after reading the guide."

The efficiency improvement brought by AI to travel is clearly visible. Long holiday travel is itself an overload of information: flights, hotels, tickets, routes, business hours and queuing times all need repeated comparison. AI compresses this scattered information into one conversation, saving the time that ordinary people lack most. From this perspective, technology lowering the cost of information search is itself a kind of inclusiveness.

But when AI moves from "helping you check" to "deciding for you," a more hidden problem emerges: recommendation itself is a business. Yin Jie, a professor at the School of Tourism of Huaqiao University, previously said in a media interview that some AI travel guides not only show advertising tendencies, but in certain dimensions this tendency is more hidden and inducement-oriented than traditional search platforms, because "recommendation" itself is a business model, and platforms will use algorithms to prioritize "paid partners," "high-commission merchants" and "self-owned supply chain products."

This is exactly where AI recommendations differ from traditional advertising. Some commercial promotions in search results are marked with the word "ad," making it clear to consumers which positions were bought with money; but if AI says, "Based on your budget, companions and accommodation needs, I recommend Hotel A more," it sounds more like a consultant who fully understands you rather than an ad slot. Commercial influence has not disappeared; it has merely retreated from a prominent position into a smooth tone.

Li Muxin, an associate professor of economics at the Shanghai Institute for Mathematics and Interdisciplinary Sciences, previously said publicly that an important value of large models is to reduce consumers' information search costs, so it is very important to maintain a clear boundary between natural recommendations and commercial payments; if commercial factors affect recommendations and consumers find it difficult to identify them, it may not only affect user trust but also further strengthen the platform's market power as an information gateway.

Compared with traditional online travel platforms, AI recommendations also have a more obvious "black box" characteristic — why A is recommended rather than B is often harder for outsiders to judge. According to media reports, a reporter asked an AI assistant to recommend hotels near a scenic area and received a quote lower than the same room type on a large online travel platform, but when verifying with hotel staff, the other party said they did not know that booking channel and instead suggested consumers "better book through platforms such as Ctrip"; another test showed that the flight price recommended by AI was about 10 yuan more expensive than on other platforms. In other words, AI has already brought users to the transaction page, but merchant awareness and fulfillment capacity matching this capability have not yet been fully established. In addition, AI also struggles to grasp in a timely manner the crowds, road conditions, weather changes and store operating status during holidays, so guides still need manual verification.

What is more worthy of vigilance is the way algorithms shape consumption itself. When itinerary, hotels, tickets and dining are packaged into the same conversation window and completed with one click, consumers' price comparison behavior is quietly dissolved. In the past, people shopped around; now they often only need to accept one "comprehensively optimal" answer. What is saved is time, and what is given up is judgment. And the National Day Golden Week is precisely the period when consumption is most concentrated in the year — Ctrip data shows that after entering the National Day Golden Week, the proportion of cross-province travel jumped to 82%, and among people choosing to combine the Mid-Autumn Festival and National Day holidays, nearly half traveled for more than 8 days. The more concentrated the consumption, the more significant a tiny deviation in algorithmic ranking becomes when magnified across massive orders.

Saying this is not to deny AI's entry into consumption scenarios. The issue has always been about boundaries: consumers have the right to know which parts of the results they see come from the algorithm's objective judgment and which come from commercial cooperation. This requires a joint answer from three parties. On the platform side, "recommendation" and "charging" should be clearly separated, and commercial cooperation should be identifiable and explainable, so consumers can distinguish whether a suggestion is calculated or "bought"; on the regulatory side, algorithm filing and labeling requirements need to extend to consumer recommendation scenarios, making transparency an access condition for traffic gateways; and as consumers, perhaps we should also leave ourselves some room to "think one step further" — treat AI's plan as a draft rather than a decision, compare prices once more before booking a room, and verify business hours once more before departure.

The meaning of a long holiday has never been just to fill the itinerary more fully. What is truly precious in a journey is where you want to go, how much you are willing to spend and what you are willing to pay for. These decisions are still made by people themselves. Algorithms can calculate the shortest route and the most cost-effective room for you, but they should not define what is "worth it." After all, a holiday is for living the life you want, not for running through an optimal solution.